Functions

Manipulation and Creation of States and Operators

Quantum States

basis(N, n=0, offset=0)[source]

Generates the vector representation of a Fock state.

Parameters:

N : int

Number of Fock states in Hilbert space.

n : int

Integer corresponding to desired number state, defaults to 0 if omitted.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the state.

Returns:

state : qobj

Qobj representing the requested number state |n>.

Notes

A subtle incompatibility with the quantum optics toolbox: In QuTiP:

basis(N, 0) = ground state

but in the qotoolbox:

basis(N, 1) = ground state

Examples

>>> basis(5,2)
Quantum object: dims = [[5], [1]], shape = [5, 1], type = ket
Qobj data =
[[ 0.+0.j]
 [ 0.+0.j]
 [ 1.+0.j]
 [ 0.+0.j]
 [ 0.+0.j]]
bell_state(state='00')[source]

Returns the Bell state:

|B00> = 1 / sqrt(2)*[|0>|0>+|1>|1>] |B01> = 1 / sqrt(2)*[|0>|0>-|1>|1>] |B10> = 1 / sqrt(2)*[|0>|1>+|1>|0>] |B11> = 1 / sqrt(2)*[|0>|1>-|1>|0>]
Returns:

Bell_state : qobj

Bell state

bra(seq, dim=2)[source]

Produces a multiparticle bra state for a list or string, where each element stands for state of the respective particle.

Parameters:

seq : str / list of ints or characters

Each element defines state of the respective particle. (e.g. [1,1,0,1] or a string “1101”). For qubits it is also possible to use the following conventions: - ‘g’/’e’ (ground and excited state) - ‘u’/’d’ (spin up and down) - ‘H’/’V’ (horizontal and vertical polarization) Note: for dimension > 9 you need to use a list.

dim : int (default: 2) / list of ints

Space dimension for each particle: int if there are the same, list if they are different.

Returns:

bra : qobj

Examples

>>> bra("10")
Quantum object: dims = [[1, 1], [2, 2]], shape = [1, 4], type = bra
Qobj data =
[[ 0.  0.  1.  0.]]
>>> bra("Hue")
Quantum object: dims = [[1, 1, 1], [2, 2, 2]], shape = [1, 8], type = bra
Qobj data =
[[ 0.  1.  0.  0.  0.  0.  0.  0.]]
>>> bra("12", 3)
Quantum object: dims = [[1, 1], [3, 3]], shape = [1, 9], type = bra
Qobj data =
[[ 0.  0.  0.  0.  0.  1.  0.  0.  0.]]
>>> bra("31", [5, 2])
Quantum object: dims = [[1, 1], [5, 2]], shape = [1, 10], type = bra
Qobj data =
[[ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]]
coherent(N, alpha, offset=0, method='operator')[source]

Generates a coherent state with eigenvalue alpha.

Constructed using displacement operator on vacuum state.

Parameters:

N : int

Number of Fock states in Hilbert space.

alpha : float/complex

Eigenvalue of coherent state.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the state. Using a non-zero offset will make the default method ‘analytic’.

method : string {‘operator’, ‘analytic’}

Method for generating coherent state.

Returns:

state : qobj

Qobj quantum object for coherent state

Notes

Select method ‘operator’ (default) or ‘analytic’. With the ‘operator’ method, the coherent state is generated by displacing the vacuum state using the displacement operator defined in the truncated Hilbert space of size ‘N’. This method guarantees that the resulting state is normalized. With ‘analytic’ method the coherent state is generated using the analytical formula for the coherent state coefficients in the Fock basis. This method does not guarantee that the state is normalized if truncated to a small number of Fock states, but would in that case give more accurate coefficients.

Examples

>>> coherent(5,0.25j)
Quantum object: dims = [[5], [1]], shape = [5, 1], type = ket
Qobj data =
[[  9.69233235e-01+0.j        ]
 [  0.00000000e+00+0.24230831j]
 [ -4.28344935e-02+0.j        ]
 [  0.00000000e+00-0.00618204j]
 [  7.80904967e-04+0.j        ]]
coherent_dm(N, alpha, offset=0, method='operator')[source]

Density matrix representation of a coherent state.

Constructed via outer product of qutip.states.coherent

Parameters:

N : int

Number of Fock states in Hilbert space.

alpha : float/complex

Eigenvalue for coherent state.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the state.

method : string {‘operator’, ‘analytic’}

Method for generating coherent density matrix.

Returns:

dm : qobj

Density matrix representation of coherent state.

Notes

Select method ‘operator’ (default) or ‘analytic’. With the ‘operator’ method, the coherent density matrix is generated by displacing the vacuum state using the displacement operator defined in the truncated Hilbert space of size ‘N’. This method guarantees that the resulting density matrix is normalized. With ‘analytic’ method the coherent density matrix is generated using the analytical formula for the coherent state coefficients in the Fock basis. This method does not guarantee that the state is normalized if truncated to a small number of Fock states, but would in that case give more accurate coefficients.

Examples

>>> coherent_dm(3,0.25j)
Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 0.93941695+0.j          0.00000000-0.23480733j -0.04216943+0.j        ]
 [ 0.00000000+0.23480733j  0.05869011+0.j          0.00000000-0.01054025j]
 [-0.04216943+0.j          0.00000000+0.01054025j  0.00189294+0.j        ]]
enr_state_dictionaries(dims, excitations)[source]

Return the number of states, and lookup-dictionaries for translating a state tuple to a state index, and vice versa, for a system with a given number of components and maximum number of excitations.

Parameters:

dims: list

A list with the number of states in each sub-system.

excitations : integer

The maximum numbers of dimension

Returns:

nstates, state2idx, idx2state: integer, dict, dict

The number of states nstates, a dictionary for looking up state indices from a state tuple, and a dictionary for looking up state state tuples from state indices.

enr_thermal_dm(dims, excitations, n)[source]

Generate the density operator for a thermal state in the excitation-number- restricted state space defined by the dims and exciations arguments. See the documentation for enr_fock for a more detailed description of these arguments. The temperature of each mode in dims is specified by the average number of excitatons n.

Parameters:

dims : list

A list of the dimensions of each subsystem of a composite quantum system.

excitations : integer

The maximum number of excitations that are to be included in the state space.

n : integer

The average number of exciations in the thermal state. n can be a float (which then applies to each mode), or a list/array of the same length as dims, in which each element corresponds specifies the temperature of the corresponding mode.

Returns:

dm : Qobj

Thermal state density matrix.

enr_fock(dims, excitations, state)[source]

Generate the Fock state representation in a excitation-number restricted state space. The dims argument is a list of integers that define the number of quantums states of each component of a composite quantum system, and the excitations specifies the maximum number of excitations for the basis states that are to be included in the state space. The state argument is a tuple of integers that specifies the state (in the number basis representation) for which to generate the Fock state representation.

Parameters:

dims : list

A list of the dimensions of each subsystem of a composite quantum system.

excitations : integer

The maximum number of excitations that are to be included in the state space.

state : list of integers

The state in the number basis representation.

Returns:

ket : Qobj

A Qobj instance that represent a Fock state in the exication-number- restricted state space defined by dims and exciations.

fock(N, n=0, offset=0)[source]

Bosonic Fock (number) state.

Same as qutip.states.basis.

Parameters:

N : int

Number of states in the Hilbert space.

n : int

int for desired number state, defaults to 0 if omitted.

Returns:

Requested number state \(\left|n\right>\).

Examples

>>> fock(4,3)
Quantum object: dims = [[4], [1]], shape = [4, 1], type = ket
Qobj data =
[[ 0.+0.j]
 [ 0.+0.j]
 [ 0.+0.j]
 [ 1.+0.j]]
fock_dm(N, n=0, offset=0)[source]

Density matrix representation of a Fock state

Constructed via outer product of qutip.states.fock.

Parameters:

N : int

Number of Fock states in Hilbert space.

n : int

int for desired number state, defaults to 0 if omitted.

Returns:

dm : qobj

Density matrix representation of Fock state.

Examples

>>> fock_dm(3,1)
Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 0.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  1.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  0.+0.j]]
ghz_state(N=3)[source]

Returns the N-qubit GHZ-state.

Parameters:

N : int (default=3)

Number of qubits in state

Returns:

G : qobj

N-qubit GHZ-state

maximally_mixed_dm(N)[source]

Returns the maximally mixed density matrix for a Hilbert space of dimension N.

Parameters:

N : int

Number of basis states in Hilbert space.

Returns:

dm : qobj

Thermal state density matrix.

ket(seq, dim=2)[source]

Produces a multiparticle ket state for a list or string, where each element stands for state of the respective particle.

Parameters:

seq : str / list of ints or characters

Each element defines state of the respective particle. (e.g. [1,1,0,1] or a string “1101”). For qubits it is also possible to use the following conventions: - ‘g’/’e’ (ground and excited state) - ‘u’/’d’ (spin up and down) - ‘H’/’V’ (horizontal and vertical polarization) Note: for dimension > 9 you need to use a list.

dim : int (default: 2) / list of ints

Space dimension for each particle: int if there are the same, list if they are different.

Returns:

ket : qobj

Examples

>>> ket("10")
Quantum object: dims = [[2, 2], [1, 1]], shape = [4, 1], type = ket
Qobj data =
[[ 0.]
 [ 0.]
 [ 1.]
 [ 0.]]
>>> ket("Hue")
Quantum object: dims = [[2, 2, 2], [1, 1, 1]], shape = [8, 1], type = ket
Qobj data =
[[ 0.]
 [ 1.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]]
>>> ket("12", 3)
Quantum object: dims = [[3, 3], [1, 1]], shape = [9, 1], type = ket
Qobj data =
[[ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 1.]
 [ 0.]
 [ 0.]
 [ 0.]]
>>> ket("31", [5, 2])
Quantum object: dims = [[5, 2], [1, 1]], shape = [10, 1], type = ket
Qobj data =
[[ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 1.]
 [ 0.]
 [ 0.]]
ket2dm(Q)[source]

Takes input ket or bra vector and returns density matrix formed by outer product.

Parameters:

Q : qobj

Ket or bra type quantum object.

Returns:

dm : qobj

Density matrix formed by outer product of Q.

Examples

>>> x=basis(3,2)
>>> ket2dm(x)
Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 0.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  1.+0.j]]
phase_basis(N, m, phi0=0)[source]

Basis vector for the mth phase of the Pegg-Barnett phase operator.

Parameters:

N : int

Number of basis vectors in Hilbert space.

m : int

Integer corresponding to the mth discrete phase phi_m=phi0+2*pi*m/N

phi0 : float (default=0)

Reference phase angle.

Returns:

state : qobj

Ket vector for mth Pegg-Barnett phase operator basis state.

Notes

The Pegg-Barnett basis states form a complete set over the truncated Hilbert space.

projection(N, n, m, offset=0)[source]

The projection operator that projects state \(|m>\) on state \(|n>\).

Parameters:

N : int

Number of basis states in Hilbert space.

n, m : float

The number states in the projection.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the projector.

Returns:

oper : qobj

Requested projection operator.

qutrit_basis()[source]

Basis states for a three level system (qutrit)

Returns:

qstates : array

Array of qutrit basis vectors

singlet_state()[source]

Returns the two particle singlet-state:

|S>=1/sqrt(2)*[|0>|1>-|1>|0>]

that is identical to the fourth bell state.

Returns:

Bell_state : qobj

|B11> Bell state

spin_state(j, m, type='ket')[source]

Generates the spin state |j, m>, i.e. the eigenstate of the spin-j Sz operator with eigenvalue m.

Parameters:

j : float

The spin of the state ().

m : int

Eigenvalue of the spin-j Sz operator.

type : string {‘ket’, ‘bra’, ‘dm’}

Type of state to generate.

Returns:

state : qobj

Qobj quantum object for spin state

spin_coherent(j, theta, phi, type='ket')[source]

Generates the spin state |j, m>, i.e. the eigenstate of the spin-j Sz operator with eigenvalue m.

Parameters:

j : float

The spin of the state.

theta : float

Angle from z axis.

phi : float

Angle from x axis.

type : string {‘ket’, ‘bra’, ‘dm’}

Type of state to generate.

Returns:

state : qobj

Qobj quantum object for spin coherent state

state_number_enumerate(dims, excitations=None, state=None, idx=0)[source]

An iterator that enumerate all the state number arrays (quantum numbers on the form [n1, n2, n3, …]) for a system with dimensions given by dims.

Example

>>> for state in state_number_enumerate([2,2]):
>>>     print(state)
[ 0  0 ]
[ 0  1 ]
[ 1  0 ]
[ 1  1 ]
Parameters:

dims : list or array

The quantum state dimensions array, as it would appear in a Qobj.

state : list

Current state in the iteration. Used internally.

excitations : integer (None)

Restrict state space to states with excitation numbers below or equal to this value.

idx : integer

Current index in the iteration. Used internally.

Returns:

state_number : list

Successive state number arrays that can be used in loops and other iterations, using standard state enumeration by definition.

state_number_index(dims, state)[source]

Return the index of a quantum state corresponding to state, given a system with dimensions given by dims.

Example

>>> state_number_index([2, 2, 2], [1, 1, 0])
6
Parameters:

dims : list or array

The quantum state dimensions array, as it would appear in a Qobj.

state : list

State number array.

Returns:

idx : int

The index of the state given by state in standard enumeration ordering.

state_index_number(dims, index)[source]

Return a quantum number representation given a state index, for a system of composite structure defined by dims.

Example

>>> state_index_number([2, 2, 2], 6)
[1, 1, 0]
Parameters:

dims : list or array

The quantum state dimensions array, as it would appear in a Qobj.

index : integer

The index of the state in standard enumeration ordering.

Returns:

state : list

The state number array corresponding to index index in standard enumeration ordering.

state_number_qobj(dims, state)[source]

Return a Qobj representation of a quantum state specified by the state array state.

Example

>>> state_number_qobj([2, 2, 2], [1, 0, 1])
Quantum object: dims = [[2, 2, 2], [1, 1, 1]], shape = [8, 1], type = ket
Qobj data =
[[ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 1.]
 [ 0.]
 [ 0.]]
Parameters:

dims : list or array

The quantum state dimensions array, as it would appear in a Qobj.

state : list

State number array.

Returns:

state : qutip.Qobj.qobj

The state as a qutip.Qobj.qobj instance.

thermal_dm(N, n, method='operator')[source]

Density matrix for a thermal state of n particles

Parameters:

N : int

Number of basis states in Hilbert space.

n : float

Expectation value for number of particles in thermal state.

method : string {‘operator’, ‘analytic’}

string that sets the method used to generate the thermal state probabilities

Returns:

dm : qobj

Thermal state density matrix.

Notes

The ‘operator’ method (default) generates the thermal state using the truncated number operator num(N). This is the method that should be used in computations. The ‘analytic’ method uses the analytic coefficients derived in an infinite Hilbert space. The analytic form is not necessarily normalized, if truncated too aggressively.

Examples

>>> thermal_dm(5, 1)
Quantum object: dims = [[5], [5]], shape = [5, 5], type = oper, isHerm = True
Qobj data =
[[ 0.51612903  0.          0.          0.          0.        ]
 [ 0.          0.25806452  0.          0.          0.        ]
 [ 0.          0.          0.12903226  0.          0.        ]
 [ 0.          0.          0.          0.06451613  0.        ]
 [ 0.          0.          0.          0.          0.03225806]]
>>> thermal_dm(5, 1, 'analytic')
Quantum object: dims = [[5], [5]], shape = [5, 5], type = oper, isHerm = True
Qobj data =
[[ 0.5      0.       0.       0.       0.     ]
 [ 0.       0.25     0.       0.       0.     ]
 [ 0.       0.       0.125    0.       0.     ]
 [ 0.       0.       0.       0.0625   0.     ]
 [ 0.       0.       0.       0.       0.03125]]
zero_ket(N, dims=None)[source]

Creates the zero ket vector with shape Nx1 and dimensions dims.

Parameters:

N : int

Hilbert space dimensionality

dims : list

Optional dimensions if ket corresponds to a composite Hilbert space.

Returns:

zero_ket : qobj

Zero ket on given Hilbert space.

Quantum Operators

This module contains functions for generating Qobj representation of a variety of commonly occuring quantum operators.

charge(Nmax, Nmin=None, frac=1)[source]

Generate the diagonal charge operator over charge states from Nmin to Nmax.

Parameters:

Nmax : int

Maximum charge state to consider.

Nmin : int (default = -Nmax)

Lowest charge state to consider.

frac : float (default = 1)

Specify fractional charge if needed.

Returns:

C : Qobj

Charge operator over [Nmin,Nmax].

Notes

New in version 3.2.

commutator(A, B, kind='normal')[source]

Return the commutator of kind kind (normal, anti) of the two operators A and B.

create(N, offset=0)[source]

Creation (raising) operator.

Parameters:

N : int

Dimension of Hilbert space.

Returns:

oper : qobj

Qobj for raising operator.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Examples

>>> create(4)
Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 0.00000000+0.j  0.00000000+0.j  0.00000000+0.j  0.00000000+0.j]
 [ 1.00000000+0.j  0.00000000+0.j  0.00000000+0.j  0.00000000+0.j]
 [ 0.00000000+0.j  1.41421356+0.j  0.00000000+0.j  0.00000000+0.j]
 [ 0.00000000+0.j  0.00000000+0.j  1.73205081+0.j  0.00000000+0.j]]
destroy(N, offset=0)[source]

Destruction (lowering) operator.

Parameters:

N : int

Dimension of Hilbert space.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Returns:

oper : qobj

Qobj for lowering operator.

Examples

>>> destroy(4)
Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 0.00000000+0.j  1.00000000+0.j  0.00000000+0.j  0.00000000+0.j]
 [ 0.00000000+0.j  0.00000000+0.j  1.41421356+0.j  0.00000000+0.j]
 [ 0.00000000+0.j  0.00000000+0.j  0.00000000+0.j  1.73205081+0.j]
 [ 0.00000000+0.j  0.00000000+0.j  0.00000000+0.j  0.00000000+0.j]]
displace(N, alpha, offset=0)[source]

Single-mode displacement operator.

Parameters:

N : int

Dimension of Hilbert space.

alpha : float/complex

Displacement amplitude.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Returns:

oper : qobj

Displacement operator.

Examples

>>> displace(4,0.25)
Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 0.96923323+0.j -0.24230859+0.j  0.04282883+0.j -0.00626025+0.j]
 [ 0.24230859+0.j  0.90866411+0.j -0.33183303+0.j  0.07418172+0.j]
 [ 0.04282883+0.j  0.33183303+0.j  0.84809499+0.j -0.41083747+0.j]
 [ 0.00626025+0.j  0.07418172+0.j  0.41083747+0.j  0.90866411+0.j]]
enr_destroy(dims, excitations)[source]

Generate annilation operators for modes in a excitation-number-restricted state space. For example, consider a system consisting of 4 modes, each with 5 states. The total hilbert space size is 5**4 = 625. If we are only interested in states that contain up to 2 excitations, we only need to include states such as

(0, 0, 0, 0) (0, 0, 0, 1) (0, 0, 0, 2) (0, 0, 1, 0) (0, 0, 1, 1) (0, 0, 2, 0) …

This function creates annihilation operators for the 4 modes that act within this state space:

a1, a2, a3, a4 = enr_destroy([5, 5, 5, 5], excitations=2)

From this point onwards, the annihiltion operators a1, …, a4 can be used to setup a Hamiltonian, collapse operators and expectation-value operators, etc., following the usual pattern.

Parameters:

dims : list

A list of the dimensions of each subsystem of a composite quantum system.

excitations : integer

The maximum number of excitations that are to be included in the state space.

Returns:

a_ops : list of qobj

A list of annihilation operators for each mode in the composite quantum system described by dims.

enr_identity(dims, excitations)[source]

Generate the identity operator for the excitation-number restricted state space defined by the dims and exciations arguments. See the docstring for enr_fock for a more detailed description of these arguments.

Parameters:

dims : list

A list of the dimensions of each subsystem of a composite quantum system.

excitations : integer

The maximum number of excitations that are to be included in the state space.

state : list of integers

The state in the number basis representation.

Returns:

op : Qobj

A Qobj instance that represent the identity operator in the exication-number-restricted state space defined by dims and exciations.

jmat(j, *args)[source]

Higher-order spin operators:

Parameters:

j : float

Spin of operator

args : str

Which operator to return ‘x’,’y’,’z’,’+’,’-‘. If no args given, then output is [‘x’,’y’,’z’]

Returns:

jmat : qobj / ndarray

qobj for requested spin operator(s).

Notes

If no ‘args’ input, then returns array of [‘x’,’y’,’z’] operators.

Examples

>>> jmat(1)
[ Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 0.          0.70710678  0.        ]
 [ 0.70710678  0.          0.70710678]
 [ 0.          0.70710678  0.        ]]
 Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 0.+0.j          0.-0.70710678j  0.+0.j        ]
 [ 0.+0.70710678j  0.+0.j          0.-0.70710678j]
 [ 0.+0.j          0.+0.70710678j  0.+0.j        ]]
 Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 1.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0. -1.]]]
num(N, offset=0)[source]

Quantum object for number operator.

Parameters:

N : int

The dimension of the Hilbert space.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Returns:

oper: qobj

Qobj for number operator.

Examples

>>> num(4)
Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[0 0 0 0]
 [0 1 0 0]
 [0 0 2 0]
 [0 0 0 3]]
qeye(N)[source]

Identity operator

Parameters:

N : int or list of ints

Dimension of Hilbert space. If provided as a list of ints, then the dimension is the product over this list, but the dims property of the new Qobj are set to this list.

Returns:

oper : qobj

Identity operator Qobj.

Examples

>>> qeye(3)
Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True
Qobj data =
[[ 1.  0.  0.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]]
identity(N)[source]

Identity operator. Alternative name to qeye.

Parameters:

N : int or list of ints

Dimension of Hilbert space. If provided as a list of ints, then the dimension is the product over this list, but the dims property of the new Qobj are set to this list.

Returns:

oper : qobj

Identity operator Qobj.

momentum(N, offset=0)[source]

Momentum operator p=-1j/sqrt(2)*(a-a.dag())

Parameters:

N : int

Number of Fock states in Hilbert space.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Returns:

oper : qobj

Momentum operator as Qobj.

phase(N, phi0=0)[source]

Single-mode Pegg-Barnett phase operator.

Parameters:

N : int

Number of basis states in Hilbert space.

phi0 : float

Reference phase.

Returns:

oper : qobj

Phase operator with respect to reference phase.

Notes

The Pegg-Barnett phase operator is Hermitian on a truncated Hilbert space.

position(N, offset=0)[source]

Position operator x=1/sqrt(2)*(a+a.dag())

Parameters:

N : int

Number of Fock states in Hilbert space.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Returns:

oper : qobj

Position operator as Qobj.

qdiags(diagonals, offsets, dims=None, shape=None)[source]

Constructs an operator from an array of diagonals.

Parameters:

diagonals : sequence of array_like

Array of elements to place along the selected diagonals.

offsets : sequence of ints

Sequence for diagonals to be set:
  • k=0 main diagonal
  • k>0 kth upper diagonal
  • k<0 kth lower diagonal

dims : list, optional

Dimensions for operator

shape : list, tuple, optional

Shape of operator. If omitted, a square operator large enough to contain the diagonals is generated.

See also

scipy.sparse.diags

Notes

This function requires SciPy 0.11+.

Examples

>>> qdiags(sqrt(range(1,4)),1)
Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isherm = False
Qobj data =
[[ 0.          1.          0.          0.        ]
 [ 0.          0.          1.41421356  0.        ]
 [ 0.          0.          0.          1.73205081]
 [ 0.          0.          0.          0.        ]]
qutrit_ops()[source]

Operators for a three level system (qutrit).

Returns:

opers: array

array of qutrit operators.

qzero(N)[source]

Zero operator

Parameters:

N : int or list of ints

Dimension of Hilbert space. If provided as a list of ints, then the dimension is the product over this list, but the dims property of the new Qobj are set to this list.

Returns:

qzero : qobj

Zero operator Qobj.

sigmam()[source]

Annihilation operator for Pauli spins.

Examples

>>> sigmam()
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = False
Qobj data =
[[ 0.  0.]
 [ 1.  0.]]
sigmap()[source]

Creation operator for Pauli spins.

Examples

>>> sigmam()
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = False
Qobj data =
[[ 0.  1.]
 [ 0.  0.]]
sigmax()[source]

Pauli spin 1/2 sigma-x operator

Examples

>>> sigmax()
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = False
Qobj data =
[[ 0.  1.]
 [ 1.  0.]]
sigmay()[source]

Pauli spin 1/2 sigma-y operator.

Examples

>>> sigmay()
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = True
Qobj data =
[[ 0.+0.j  0.-1.j]
 [ 0.+1.j  0.+0.j]]
sigmaz()[source]

Pauli spin 1/2 sigma-z operator.

Examples

>>> sigmaz()
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = True
Qobj data =
[[ 1.  0.]
 [ 0. -1.]]
spin_Jx(j)[source]

Spin-j x operator

Parameters:

j : float

Spin of operator

Returns:

op : Qobj

qobj representation of the operator.

spin_Jy(j)[source]

Spin-j y operator

Parameters:

j : float

Spin of operator

Returns:

op : Qobj

qobj representation of the operator.

spin_Jz(j)[source]

Spin-j z operator

Parameters:

j : float

Spin of operator

Returns:

op : Qobj

qobj representation of the operator.

spin_Jm(j)[source]

Spin-j annihilation operator

Parameters:

j : float

Spin of operator

Returns:

op : Qobj

qobj representation of the operator.

spin_Jp(j)[source]

Spin-j creation operator

Parameters:

j : float

Spin of operator

Returns:

op : Qobj

qobj representation of the operator.

squeeze(N, z, offset=0)[source]

Single-mode Squeezing operator.

Parameters:

N : int

Dimension of hilbert space.

z : float/complex

Squeezing parameter.

offset : int (default 0)

The lowest number state that is included in the finite number state representation of the operator.

Returns:

oper : qutip.qobj.Qobj

Squeezing operator.

Examples

>>> squeeze(4, 0.25)
Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 0.98441565+0.j  0.00000000+0.j  0.17585742+0.j  0.00000000+0.j]
 [ 0.00000000+0.j  0.95349007+0.j  0.00000000+0.j  0.30142443+0.j]
 [-0.17585742+0.j  0.00000000+0.j  0.98441565+0.j  0.00000000+0.j]
 [ 0.00000000+0.j -0.30142443+0.j  0.00000000+0.j  0.95349007+0.j]]
squeezing(a1, a2, z)[source]

Generalized squeezing operator.

\[S(z) = \exp\left(\frac{1}{2}\left(z^*a_1a_2 - za_1^\dagger a_2^\dagger\right)\right)\]
Parameters:

a1 : qutip.qobj.Qobj

Operator 1.

a2 : qutip.qobj.Qobj

Operator 2.

z : float/complex

Squeezing parameter.

Returns:

oper : qutip.qobj.Qobj

Squeezing operator.

tunneling(N, m=1)[source]

Tunneling operator with elements of the form \(\sum |N><N+m| + |N+m><N|\).

Parameters:

N : int

Number of basis states in Hilbert space.

m : int (default = 1)

Number of excitations in tunneling event.

Returns:

T : Qobj

Tunneling operator.

Notes

New in version 3.2.

Random Operators and States

This module is a collection of random state and operator generators. The sparsity of the ouput Qobj’s is controlled by varing the density parameter.

rand_dm(N, density=0.75, pure=False, dims=None)[source]

Creates a random NxN density matrix.

Parameters:

N : int, ndarray, list

If int, then shape of output operator. If list/ndarray then eigenvalues of generated density matrix.

density : float

Density between [0,1] of output density matrix.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N],[N]].

Returns:

oper : qobj

NxN density matrix quantum operator.

Notes

For small density matrices., choosing a low density will result in an error as no diagonal elements will be generated such that \(Tr(\rho)=1\).

rand_dm_ginibre(N=2, rank=None, dims=None)[source]

Returns a Ginibre random density operator of dimension dim and rank rank by using the algorithm of [BCSZ08]. If rank is None, a full-rank (Hilbert-Schmidt ensemble) random density operator will be returned.

Parameters:

N : int

Dimension of the density operator to be returned.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N],[N]].

rank : int or None

Rank of the sampled density operator. If None, a full-rank density operator is generated.

Returns:

rho : Qobj

An N × N density operator sampled from the Ginibre or Hilbert-Schmidt distribution.

rand_dm_hs(N=2, dims=None)[source]

Returns a Hilbert-Schmidt random density operator of dimension dim and rank rank by using the algorithm of [BCSZ08].

Parameters:

N : int

Dimension of the density operator to be returned.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N],[N]].

Returns:

rho : Qobj

A dim × dim density operator sampled from the Ginibre or Hilbert-Schmidt distribution.

rand_herm(N, density=0.75, dims=None, pos_def=False)[source]

Creates a random NxN sparse Hermitian quantum object.

If ‘N’ is an integer, uses \(H=0.5*(X+X^{+})\) where \(X\) is a randomly generated quantum operator with a given density. Else uses complex Jacobi rotations when ‘N’ is given by an array.

Parameters:

N : int, list/ndarray

If int, then shape of output operator. If list/ndarray then eigenvalues of generated operator.

density : float

Density between [0,1] of output Hermitian operator.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N],[N]].

pos_def : bool (default=False)

Return a positive semi-definite matrix (by diagonal dominance).

Returns:

oper : qobj

NxN Hermitian quantum operator.

rand_ket(N, density=1, dims=None)[source]

Creates a random Nx1 sparse ket vector.

Parameters:

N : int

Number of rows for output quantum operator.

density : float

Density between [0,1] of output ket state.

dims : list

Left-dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N]].

Returns:

oper : qobj

Nx1 ket state quantum operator.

rand_ket_haar(N=2, dims=None)[source]

Returns a Haar random pure state of dimension dim by applying a Haar random unitary to a fixed pure state.

Parameters:

N : int

Dimension of the state vector to be returned.

dims : list of ints, or None

Left-dimensions of the resultant quantum object. If None, [N] is used.

Returns:

psi : Qobj

A random state vector drawn from the Haar measure.

rand_unitary(N, density=0.75, dims=None)[source]

Creates a random NxN sparse unitary quantum object.

Uses \(\exp(-iH)\) where H is a randomly generated Hermitian operator.

Parameters:

N : int

Shape of output quantum operator.

density : float

Density between [0,1] of output Unitary operator.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N],[N]].

Returns:

oper : qobj

NxN Unitary quantum operator.

rand_unitary_haar(N=2, dims=None)[source]

Returns a Haar random unitary matrix of dimension dim, using the algorithm of [Mez07].

Parameters:

N : int

Dimension of the unitary to be returned.

dims : list of lists of int, or None

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[N],[N]].

Returns:

U : Qobj

Unitary of dims [[dim], [dim]] drawn from the Haar measure.

rand_super(N=5, dims=None)[source]

Returns a randomly drawn superoperator acting on operators acting on N dimensions.

Parameters:

N : int

Square root of the dimension of the superoperator to be returned.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[[N],[N]], [[N],[N]]].

rand_super_bcsz(N=2, enforce_tp=True, rank=None, dims=None)[source]

Returns a random superoperator drawn from the Bruzda et al ensemble for CPTP maps [BCSZ08]. Note that due to finite numerical precision, for ranks less than full-rank, zero eigenvalues may become slightly negative, such that the returned operator is not actually completely positive.

Parameters:

N : int

Square root of the dimension of the superoperator to be returned.

enforce_tp : bool

If True, the trace-preserving condition of [BCSZ08] is enforced; otherwise only complete positivity is enforced.

rank : int or None

Rank of the sampled superoperator. If None, a full-rank superoperator is generated.

dims : list

Dimensions of quantum object. Used for specifying tensor structure. Default is dims=[[[N],[N]], [[N],[N]]].

Returns:

rho : Qobj

A superoperator acting on vectorized dim × dim density operators, sampled from the BCSZ distribution.

Three-Level Atoms

This module provides functions that are useful for simulating the three level atom with QuTiP. A three level atom (qutrit) has three states, which are linked by dipole transitions so that 1 <-> 2 <-> 3. Depending on there relative energies they are in the ladder, lambda or vee configuration. The structure of the relevant operators is the same for any of the three configurations:

Ladder:          Lambda:                 Vee:
                            |two>                       |three>
  -------|three>           -------                      -------
     |                       / \             |one>         /
     |                      /   \           -------       /
     |                     /     \             \         /
  -------|two>            /       \             \       /
     |                   /         \             \     /
     |                  /           \             \   /
     |                 /        --------           \ /
  -------|one>      -------      |three>         -------
                     |one>                       |two>

References

The naming of qutip operators follows the convention in [R1] .

[R1]Shore, B. W., “The Theory of Coherent Atomic Excitation”, Wiley, 1990.

Notes

Contributed by Markus Baden, Oct. 07, 2011

three_level_basis()[source]

Basis states for a three level atom.

Returns:

states : array

array of three level atom basis vectors.

three_level_ops()[source]

Operators for a three level system (qutrit)

Returns:

ops : array

array of three level operators.

Superoperators and Liouvillians

operator_to_vector(op)[source]

Create a vector representation of a quantum operator given the matrix representation.

vector_to_operator(op)[source]

Create a matrix representation given a quantum operator in vector form.

liouvillian(H, c_ops=[], data_only=False, chi=None)[source]

Assembles the Liouvillian superoperator from a Hamiltonian and a list of collapse operators. Like liouvillian, but with an experimental implementation which avoids creating extra Qobj instances, which can be advantageous for large systems.

Parameters:

H : qobj

System Hamiltonian.

c_ops : array_like

A list or array of collapse operators.

Returns:

L : qobj

Liouvillian superoperator.

spost(A)[source]

Superoperator formed from post-multiplication by operator A

Parameters:

A : qobj

Quantum operator for post multiplication.

Returns:

super : qobj

Superoperator formed from input qauntum object.

spre(A)[source]

Superoperator formed from pre-multiplication by operator A.

Parameters:

A : qobj

Quantum operator for pre-multiplication.

Returns:

super :qobj

Superoperator formed from input quantum object.

sprepost(A, B)[source]

Superoperator formed from pre-multiplication by operator A and post- multiplication of operator B.

Parameters:

A : Qobj

Quantum operator for pre-multiplication.

B : Qobj

Quantum operator for post-multiplication.

Returns:

super : Qobj

Superoperator formed from input quantum objects.

lindblad_dissipator(a, b=None, data_only=False)[source]

Lindblad dissipator (generalized) for a single pair of collapse operators (a, b), or for a single collapse operator (a) when b is not specified:

\[\mathcal{D}[a,b]\rho = a \rho b^\dagger - \frac{1}{2}a^\dagger b\rho - \frac{1}{2}\rho a^\dagger b\]
Parameters:

a : qobj

Left part of collapse operator.

b : qobj (optional)

Right part of collapse operator. If not specified, b defaults to a.

Returns:

D : qobj

Lindblad dissipator superoperator.

Superoperator Representations

This module implements transformations between superoperator representations, including supermatrix, Kraus, Choi and Chi (process) matrix formalisms.

to_choi(q_oper)[source]

Converts a Qobj representing a quantum map to the Choi representation, such that the trace of the returned operator is equal to the dimension of the system.

Parameters:

q_oper : Qobj

Superoperator to be converted to Choi representation. If q_oper is type="oper", then it is taken to act by conjugation, such that to_choi(A) == to_choi(sprepost(A, A.dag())).

Returns:

choi : Qobj

A quantum object representing the same map as q_oper, such that choi.superrep == "choi".

Raises:

TypeError: if the given quantum object is not a map, or cannot be converted

to Choi representation.

to_super(q_oper)[source]

Converts a Qobj representing a quantum map to the supermatrix (Liouville) representation.

Parameters:

q_oper : Qobj

Superoperator to be converted to supermatrix representation. If q_oper is type="oper", then it is taken to act by conjugation, such that to_super(A) == sprepost(A, A.dag()).

Returns:

superop : Qobj

A quantum object representing the same map as q_oper, such that superop.superrep == "super".

Raises:

TypeError

If the given quantum object is not a map, or cannot be converted to supermatrix representation.

to_kraus(q_oper)[source]

Converts a Qobj representing a quantum map to a list of quantum objects, each representing an operator in the Kraus decomposition of the given map.

Parameters:

q_oper : Qobj

Superoperator to be converted to Kraus representation. If q_oper is type="oper", then it is taken to act by conjugation, such that to_kraus(A) == to_kraus(sprepost(A, A.dag())) == [A].

Returns:

kraus_ops : list of Qobj

A list of quantum objects, each representing a Kraus operator in the decomposition of q_oper.

Raises:

TypeError: if the given quantum object is not a map, or cannot be

decomposed into Kraus operators.

Functions acting on states and operators

Expectation Values

expect(oper, state)[source]

Calculates the expectation value for operator(s) and state(s).

Parameters:

oper : qobj/array-like

A single or a list or operators for expectation value.

state : qobj/array-like

A single or a list of quantum states or density matrices.

Returns:

expt : float/complex/array-like

Expectation value. real if oper is Hermitian, complex otherwise. A (nested) array of expectaction values of state or operator are arrays.

Examples

>>> expect(num(4), basis(4, 3))
3
variance(oper, state)[source]

Variance of an operator for the given state vector or density matrix.

Parameters:

oper : qobj

Operator for expectation value.

state : qobj/list

A single or list of quantum states or density matrices..

Returns:

var : float

Variance of operator ‘oper’ for given state.

Tensor

Module for the creation of composite quantum objects via the tensor product.

tensor(*args)[source]

Calculates the tensor product of input operators.

Parameters:

args : array_like

list or array of quantum objects for tensor product.

Returns:

obj : qobj

A composite quantum object.

Examples

>>> tensor([sigmax(), sigmax()])
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 0.+0.j  0.+0.j  0.+0.j  1.+0.j]
 [ 0.+0.j  0.+0.j  1.+0.j  0.+0.j]
 [ 0.+0.j  1.+0.j  0.+0.j  0.+0.j]
 [ 1.+0.j  0.+0.j  0.+0.j  0.+0.j]]
super_tensor(*args)[source]

Calculates the tensor product of input superoperators, by tensoring together the underlying Hilbert spaces on which each vectorized operator acts.

Parameters:

args : array_like

list or array of quantum objects with type="super".

Returns:

obj : qobj

A composite quantum object.

composite(*args)[source]

Given two or more operators, kets or bras, returns the Qobj corresponding to a composite system over each argument. For ordinary operators and vectors, this is the tensor product, while for superoperators and vectorized operators, this is the column-reshuffled tensor product.

If a mix of Qobjs supported on Hilbert and Liouville spaces are passed in, the former are promoted. Ordinary operators are assumed to be unitaries, and are promoted using to_super, while kets and bras are promoted by taking their projectors and using operator_to_vector(ket2dm(arg)).

tensor_contract(qobj, *pairs)[source]

Contracts a qobj along one or more index pairs. Note that this uses dense representations and thus should not be used for very large Qobjs.

Parameters:

pairs : tuple

One or more tuples (i, j) indicating that the i and j dimensions of the original qobj should be contracted.

Returns:

cqobj : Qobj

The original Qobj with all named index pairs contracted away.

Partial Transpose

partial_transpose(rho, mask, method='dense')[source]

Return the partial transpose of a Qobj instance rho, where mask is an array/list with length that equals the number of components of rho (that is, the length of rho.dims[0]), and the values in mask indicates whether or not the corresponding subsystem is to be transposed. The elements in mask can be boolean or integers 0 or 1, where True/1 indicates that the corresponding subsystem should be tranposed.

Parameters:

rho : qutip.qobj

A density matrix.

mask : list / array

A mask that selects which subsystems should be transposed.

method : str

choice of method, dense or sparse. The default method is dense. The sparse implementation can be faster for large and sparse systems (hundreds of quantum states).

Returns:

rho_pr: qutip.qobj

A density matrix with the selected subsystems transposed.

Entropy Functions

concurrence(rho)[source]

Calculate the concurrence entanglement measure for a two-qubit state.

Parameters:

state : qobj

Ket, bra, or density matrix for a two-qubit state.

Returns:

concur : float

Concurrence

References

[R22]http://en.wikipedia.org/wiki/Concurrence_(quantum_computing)
entropy_conditional(rho, selB, base=2.718281828459045, sparse=False)[source]

Calculates the conditional entropy \(S(A|B)=S(A,B)-S(B)\) of a selected density matrix component.

Parameters:

rho : qobj

Density matrix of composite object

selB : int/list

Selected components for density matrix B

base : {e,2}

Base of logarithm.

sparse : {False,True}

Use sparse eigensolver.

Returns:

ent_cond : float

Value of conditional entropy

entropy_linear(rho)[source]

Linear entropy of a density matrix.

Parameters:

rho : qobj

sensity matrix or ket/bra vector.

Returns:

entropy : float

Linear entropy of rho.

Examples

>>> rho=0.5*fock_dm(2,0)+0.5*fock_dm(2,1)
>>> entropy_linear(rho)
0.5
entropy_mutual(rho, selA, selB, base=2.718281828459045, sparse=False)[source]

Calculates the mutual information S(A:B) between selection components of a system density matrix.

Parameters:

rho : qobj

Density matrix for composite quantum systems

selA : int/list

int or list of first selected density matrix components.

selB : int/list

int or list of second selected density matrix components.

base : {e,2}

Base of logarithm.

sparse : {False,True}

Use sparse eigensolver.

Returns:

ent_mut : float

Mutual information between selected components.

entropy_vn(rho, base=2.718281828459045, sparse=False)[source]

Von-Neumann entropy of density matrix

Parameters:

rho : qobj

Density matrix.

base : {e,2}

Base of logarithm.

sparse : {False,True}

Use sparse eigensolver.

Returns:

entropy : float

Von-Neumann entropy of rho.

Examples

>>> rho=0.5*fock_dm(2,0)+0.5*fock_dm(2,1)
>>> entropy_vn(rho,2)
1.0

Density Matrix Metrics

This module contains a collection of functions for calculating metrics (distance measures) between states and operators.

fidelity(A, B)[source]

Calculates the fidelity (pseudo-metric) between two density matrices. See: Nielsen & Chuang, “Quantum Computation and Quantum Information”

Parameters:

A : qobj

Density matrix or state vector.

B : qobj

Density matrix or state vector with same dimensions as A.

Returns:

fid : float

Fidelity pseudo-metric between A and B.

Examples

>>> x = fock_dm(5,3)
>>> y = coherent_dm(5,1)
>>> fidelity(x,y)
0.24104350624628332
tracedist(A, B, sparse=False, tol=0)[source]

Calculates the trace distance between two density matrices.. See: Nielsen & Chuang, “Quantum Computation and Quantum Information”

Parameters:

A : qobj

Density matrix or state vector.

B : qobj

Density matrix or state vector with same dimensions as A.

tol : float

Tolerance used by sparse eigensolver, if used. (0=Machine precision)

sparse : {False, True}

Use sparse eigensolver.

Returns:

tracedist : float

Trace distance between A and B.

Examples

>>> x=fock_dm(5,3)
>>> y=coherent_dm(5,1)
>>> tracedist(x,y)
0.9705143161472971
bures_dist(A, B)[source]

Returns the Bures distance between two density matrices A & B.

The Bures distance ranges from 0, for states with unit fidelity, to sqrt(2).

Parameters:

A : qobj

Density matrix or state vector.

B : qobj

Density matrix or state vector with same dimensions as A.

Returns:

dist : float

Bures distance between density matrices.

bures_angle(A, B)[source]

Returns the Bures Angle between two density matrices A & B.

The Bures angle ranges from 0, for states with unit fidelity, to pi/2.

Parameters:

A : qobj

Density matrix or state vector.

B : qobj

Density matrix or state vector with same dimensions as A.

Returns:

angle : float

Bures angle between density matrices.

hilbert_dist(A, B)[source]

Returns the Hilbert-Schmidt distance between two density matrices A & B.

Parameters:

A : qobj

Density matrix or state vector.

B : qobj

Density matrix or state vector with same dimensions as A.

Returns:

dist : float

Hilbert-Schmidt distance between density matrices.

Notes

See V. Vedral and M. B. Plenio, Phys. Rev. A 57, 1619 (1998).

average_gate_fidelity(oper, target=None)[source]

Given a Qobj representing the supermatrix form of a map, returns the average gate fidelity (pseudo-metric) of that map.

Parameters:

A : Qobj

Quantum object representing a superoperator.

target : Qobj

Quantum object representing the target unitary; the inverse is applied before evaluating the fidelity.

Returns:

fid : float

Fidelity pseudo-metric between A and the identity superoperator, or between A and the target superunitary.

process_fidelity(U1, U2, normalize=True)[source]

Calculate the process fidelity given two process operators.

Continous Variables

This module contains a collection functions for calculating continuous variable quantities from fock-basis representation of the state of multi-mode fields.

correlation_matrix(basis, rho=None)[source]

Given a basis set of operators \(\{a\}_n\), calculate the correlation matrix:

\[C_{mn} = \langle a_m a_n \rangle\]
Parameters:

basis : list

List of operators that defines the basis for the correlation matrix.

rho : Qobj

Density matrix for which to calculate the correlation matrix. If rho is None, then a matrix of correlation matrix operators is returned instead of expectation values of those operators.

Returns:

corr_mat : ndarray

A 2-dimensional array of correlation values or operators.

covariance_matrix(basis, rho, symmetrized=True)[source]

Given a basis set of operators \(\{a\}_n\), calculate the covariance matrix:

\[V_{mn} = \frac{1}{2}\langle a_m a_n + a_n a_m \rangle - \langle a_m \rangle \langle a_n\rangle\]

or, if of the optional argument symmetrized=False,

\[V_{mn} = \langle a_m a_n\rangle - \langle a_m \rangle \langle a_n\rangle\]
Parameters:

basis : list

List of operators that defines the basis for the covariance matrix.

rho : Qobj

Density matrix for which to calculate the covariance matrix.

symmetrized : bool {True, False}

Flag indicating whether the symmetrized (default) or non-symmetrized correlation matrix is to be calculated.

Returns:

corr_mat : ndarray

A 2-dimensional array of covariance values.

correlation_matrix_field(a1, a2, rho=None)[source]

Calculates the correlation matrix for given field operators \(a_1\) and \(a_2\). If a density matrix is given the expectation values are calculated, otherwise a matrix with operators is returned.

Parameters:

a1 : Qobj

Field operator for mode 1.

a2 : Qobj

Field operator for mode 2.

rho : Qobj

Density matrix for which to calculate the covariance matrix.

Returns:

cov_mat : ndarray

Array of complex numbers or Qobj’s A 2-dimensional array of covariance values, or, if rho=0, a matrix of operators.

correlation_matrix_quadrature(a1, a2, rho=None)[source]

Calculate the quadrature correlation matrix with given field operators \(a_1\) and \(a_2\). If a density matrix is given the expectation values are calculated, otherwise a matrix with operators is returned.

Parameters:

a1 : Qobj

Field operator for mode 1.

a2 : Qobj

Field operator for mode 2.

rho : Qobj

Density matrix for which to calculate the covariance matrix.

Returns:

corr_mat : ndarray

Array of complex numbers or Qobj’s A 2-dimensional array of covariance values for the field quadratures, or, if rho=0, a matrix of operators.

wigner_covariance_matrix(a1=None, a2=None, R=None, rho=None)[source]

Calculates the Wigner covariance matrix \(V_{ij} = \frac{1}{2}(R_{ij} + R_{ji})\), given the quadrature correlation matrix \(R_{ij} = \langle R_{i} R_{j}\rangle - \langle R_{i}\rangle \langle R_{j}\rangle\), where \(R = (q_1, p_1, q_2, p_2)^T\) is the vector with quadrature operators for the two modes.

Alternatively, if R = None, and if annihilation operators a1 and a2 for the two modes are supplied instead, the quadrature correlation matrix is constructed from the annihilation operators before then the covariance matrix is calculated.

Parameters:

a1 : Qobj

Field operator for mode 1.

a2 : Qobj

Field operator for mode 2.

R : ndarray

The quadrature correlation matrix.

rho : Qobj

Density matrix for which to calculate the covariance matrix.

Returns:

cov_mat : ndarray

A 2-dimensional array of covariance values.

logarithmic_negativity(V)[source]

Calculates the logarithmic negativity given a symmetrized covariance matrix, see qutip.continous_variables.covariance_matrix. Note that the two-mode field state that is described by V must be Gaussian for this function to applicable.

Parameters:

V : 2d array

The covariance matrix.

Returns:

N : float

The logarithmic negativity for the two-mode Gaussian state that is described by the the Wigner covariance matrix V.

Dynamics and Time-Evolution

Schrödinger Equation

This module provides solvers for the unitary Schrodinger equation.

sesolve(H, rho0, tlist, e_ops=[], args={}, options=None, progress_bar=<qutip.ui.progressbar.BaseProgressBar object>, _safe_mode=True)[source]

Schrodinger equation evolution of a state vector for a given Hamiltonian.

Evolve the state vector or density matrix (rho0) using a given Hamiltonian (H), by integrating the set of ordinary differential equations that define the system.

The output is either the state vector at arbitrary points in time (tlist), or the expectation values of the supplied operators (e_ops). If e_ops is a callback function, it is invoked for each time in tlist with time and the state as arguments, and the function does not use any return values.

Parameters:

H : qutip.qobj

system Hamiltonian, or a callback function for time-dependent Hamiltonians.

rho0 : qutip.qobj

initial density matrix or state vector (ket).

tlist : list / array

list of times for \(t\).

e_ops : list of qutip.qobj / callback function single

single operator or list of operators for which to evaluate expectation values.

args : dictionary

dictionary of parameters for time-dependent Hamiltonians and collapse operators.

options : qutip.Qdeoptions

with options for the ODE solver.

Returns:

output: qutip.solver

An instance of the class qutip.solver, which contains either an array of expectation values for the times specified by tlist, or an array or state vectors or density matrices corresponding to the times in tlist [if e_ops is an empty list], or nothing if a callback function was given inplace of operators for which to calculate the expectation values.

Master Equation

This module provides solvers for the Lindblad master equation and von Neumann equation.

mesolve(H, rho0, tlist, c_ops=[], e_ops=[], args={}, options=None, progress_bar=None, _safe_mode=True)[source]

Master equation evolution of a density matrix for a given Hamiltonian and set of collapse operators, or a Liouvillian.

Evolve the state vector or density matrix (rho0) using a given Hamiltonian (H) and an [optional] set of collapse operators (c_ops), by integrating the set of ordinary differential equations that define the system. In the absence of collapse operators the system is evolved according to the unitary evolution of the Hamiltonian.

The output is either the state vector at arbitrary points in time (tlist), or the expectation values of the supplied operators (e_ops). If e_ops is a callback function, it is invoked for each time in tlist with time and the state as arguments, and the function does not use any return values.

If either H or the Qobj elements in c_ops are superoperators, they will be treated as direct contributions to the total system Liouvillian. This allows to solve master equations that are not on standard Lindblad form by passing a custom Liouvillian in place of either the H or c_ops elements.

Time-dependent operators

For time-dependent problems, H and c_ops can be callback functions that takes two arguments, time and args, and returns the Hamiltonian or Liouvillian for the system at that point in time (callback format).

Alternatively, H and c_ops can be a specified in a nested-list format where each element in the list is a list of length 2, containing an operator (qutip.qobj) at the first element and where the second element is either a string (list string format), a callback function (list callback format) that evaluates to the time-dependent coefficient for the corresponding operator, or a NumPy array (list array format) which specifies the value of the coefficient to the corresponding operator for each value of t in tlist.

Examples

H = [[H0, ‘sin(w*t)’], [H1, ‘sin(2*w*t)’]]

H = [[H0, f0_t], [H1, f1_t]]

where f0_t and f1_t are python functions with signature f_t(t, args).

H = [[H0, np.sin(w*tlist)], [H1, np.sin(2*w*tlist)]]

In the list string format and list callback format, the string expression and the callback function must evaluate to a real or complex number (coefficient for the corresponding operator).

In all cases of time-dependent operators, args is a dictionary of parameters that is used when evaluating operators. It is passed to the callback functions as second argument.

Additional options

Additional options to mesolve can be set via the options argument, which should be an instance of qutip.solver.Options. Many ODE integration options can be set this way, and the store_states and store_final_state options can be used to store states even though expectation values are requested via the e_ops argument.

Note

If an element in the list-specification of the Hamiltonian or the list of collapse operators are in superoperator form it will be added to the total Liouvillian of the problem with out further transformation. This allows for using mesolve for solving master equations that are not on standard Lindblad form.

Note

On using callback function: mesolve transforms all qutip.qobj objects to sparse matrices before handing the problem to the integrator function. In order for your callback function to work correctly, pass all qutip.qobj objects that are used in constructing the Hamiltonian via args. mesolve will check for qutip.qobj in args and handle the conversion to sparse matrices. All other qutip.qobj objects that are not passed via args will be passed on to the integrator in scipy which will raise an NotImplemented exception.

Parameters:

H : qutip.Qobj

System Hamiltonian, or a callback function for time-dependent Hamiltonians, or alternatively a system Liouvillian.

rho0 : qutip.Qobj

initial density matrix or state vector (ket).

tlist : list / array

list of times for \(t\).

c_ops : list of qutip.Qobj

single collapse operator, or list of collapse operators, or a list of Liouvillian superoperators.

e_ops : list of qutip.Qobj / callback function single

single operator or list of operators for which to evaluate expectation values.

args : dictionary

dictionary of parameters for time-dependent Hamiltonians and collapse operators.

options : qutip.Options

with options for the solver.

progress_bar : BaseProgressBar

Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation.

Returns:

result: qutip.Result

An instance of the class qutip.Result, which contains either an array result.expect of expectation values for the times specified by tlist, or an array result.states of state vectors or density matrices corresponding to the times in tlist [if e_ops is an empty list], or nothing if a callback function was given in place of operators for which to calculate the expectation values.

Monte Carlo Evolution

mcsolve(H, psi0, tlist, c_ops=[], e_ops=[], ntraj=None, args={}, options=None, progress_bar=True, map_func=None, map_kwargs=None, _safe_mode=True)[source]

Monte Carlo evolution of a state vector \(|\psi \rangle\) for a given Hamiltonian and sets of collapse operators, and possibly, operators for calculating expectation values. Options for the underlying ODE solver are given by the Options class.

mcsolve supports time-dependent Hamiltonians and collapse operators using either Python functions of strings to represent time-dependent coefficients. Note that, the system Hamiltonian MUST have at least one constant term.

As an example of a time-dependent problem, consider a Hamiltonian with two terms H0 and H1, where H1 is time-dependent with coefficient sin(w*t), and collapse operators C0 and C1, where C1 is time-dependent with coeffcient exp(-a*t). Here, w and a are constant arguments with values W and A.

Using the Python function time-dependent format requires two Python functions, one for each collapse coefficient. Therefore, this problem could be expressed as:

def H1_coeff(t,args):
    return sin(args['w']*t)

def C1_coeff(t,args):
    return exp(-args['a']*t)

H = [H0, [H1, H1_coeff]]

c_ops = [C0, [C1, C1_coeff]]

args={'a': A, 'w': W}

or in String (Cython) format we could write:

H = [H0, [H1, 'sin(w*t)']]

c_ops = [C0, [C1, 'exp(-a*t)']]

args={'a': A, 'w': W}

Constant terms are preferably placed first in the Hamiltonian and collapse operator lists.

Parameters:

H : qutip.Qobj

System Hamiltonian.

psi0 : qutip.Qobj

Initial state vector

tlist : array_like

Times at which results are recorded.

ntraj : int

Number of trajectories to run.

c_ops : array_like

single collapse operator or list or array of collapse operators.

e_ops : array_like

single operator or list or array of operators for calculating expectation values.

args : dict

Arguments for time-dependent Hamiltonian and collapse operator terms.

options : Options

Instance of ODE solver options.

progress_bar: BaseProgressBar

Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Set to None to disable the progress bar.

map_func: function

A map function for managing the calls to the single-trajactory solver.

map_kwargs: dictionary

Optional keyword arguments to the map_func function.

Returns:

results : qutip.solver.Result

Object storing all results from the simulation.

Note

It is possible to reuse the random number seeds from a previous run of the mcsolver by passing the output Result object seeds via the Options class, i.e. Options(seeds=prev_result.seeds).

Exponential Series

essolve(H, rho0, tlist, c_op_list, e_ops)[source]

Evolution of a state vector or density matrix (rho0) for a given Hamiltonian (H) and set of collapse operators (c_op_list), by expressing the ODE as an exponential series. The output is either the state vector at arbitrary points in time (tlist), or the expectation values of the supplied operators (e_ops).

Parameters:

H : qobj/function_type

System Hamiltonian.

rho0 : qutip.qobj

Initial state density matrix.

tlist : list/array

list of times for \(t\).

c_op_list : list of qutip.qobj

list of qutip.qobj collapse operators.

e_ops : list of qutip.qobj

list of qutip.qobj operators for which to evaluate expectation values.

Returns:

expt_array : array

Expectation values of wavefunctions/density matrices for the times specified in tlist.

Note

This solver does not support time-dependent Hamiltonians.

ode2es(L, rho0)[source]

Creates an exponential series that describes the time evolution for the initial density matrix (or state vector) rho0, given the Liouvillian (or Hamiltonian) L.

Parameters:

L : qobj

Liouvillian of the system.

rho0 : qobj

Initial state vector or density matrix.

Returns:

eseries : qutip.eseries

eseries represention of the system dynamics.

Bloch-Redfield Master Equation

brmesolve(H, psi0, tlist, a_ops=[], e_ops=[], c_ops=[], args={}, use_secular=True, sec_cutoff=0.1, tol=1e-12, spectra_cb=None, options=None, progress_bar=None, _safe_mode=True)[source]

Solves for the dynamics of a system using the Bloch-Redfield master equation, given an input Hamiltonian, Hermitian bath-coupling terms and their associated spectrum functions, as well as possible Lindblad collapse operators.

For time-independent systems, the Hamiltonian must be given as a Qobj, whereas the bath-coupling terms (a_ops), must be written as a nested list of operator - spectrum function pairs, where the frequency is specified by the w variable.

Example

a_ops = [[a+a.dag(),lambda w: 0.2*(w>=0)]]

For time-dependent systems, the Hamiltonian, a_ops, and Lindblad collapse operators (c_ops), can be specified in the QuTiP string-based time-dependent format. For the a_op spectra, the frequency variable must be w, and the string cannot contain any other variables other than the possibility of having a time-dependence through the time variable t:

Example

a_ops = [[a+a.dag(), ‘0.2*exp(-t)*(w>=0)’]]

It is also possible to use Cubic_Spline objects for time-dependence. In the case of a_ops, Cubic_Splines must be passed as a tuple:

Example

a_ops = [ [a+a.dag(), ( f(w), g(t)] ]

where f(w) and g(t) are strings or Cubic_spline objects for the bath spectrum and time-dependence, respectively.

Finally, if one has bath-couplimg terms of the form H = f(t)*a + conj[f(t)]*a.dag(), then the correct input format is

Example

a_ops = [ [(a,a.dag()), (f(w), g1(t), g2(t))],… ]

where f(w) is the spectrum of the operators while g1(t) and g2(t) are the time-dependence of the operators a and a.dag(), respectively

Parameters:

H : Qobj / list

System Hamiltonian given as a Qobj or nested list in string-based format.

psi0: Qobj

Initial density matrix or state vector (ket).

tlist : array_like

List of times for evaluating evolution

a_ops : list

Nested list of Hermitian system operators that couple to the bath degrees of freedom, along with their associated spectra.

e_ops : list

List of operators for which to evaluate expectation values.

c_ops : list

List of system collapse operators, or nested list in string-based format.

args : dict (not implimented)

Placeholder for future implementation, kept for API consistency.

use_secular : bool {True}

Use secular approximation when evaluating bath-coupling terms.

sec_cutoff : float {0.1}

Cutoff for secular approximation.

tol : float {qutip.setttings.atol}

Tolerance used for removing small values after basis transformation.

spectra_cb : list

DEPRECIATED. Do not use.

options : qutip.solver.Options

Options for the solver.

progress_bar : BaseProgressBar

Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation.

Returns:

result: qutip.solver.Result

An instance of the class qutip.solver.Result, which contains either an array of expectation values, for operators given in e_ops, or a list of states for the times specified by tlist.

bloch_redfield_tensor(H, a_ops, spectra_cb=None, c_ops=[], use_secular=True, sec_cutoff=0.1)[source]

Calculate the Bloch-Redfield tensor for a system given a set of operators and corresponding spectral functions that describes the system’s coupling to its environment.

Note

This tensor generation requires a time-independent Hamiltonian.

Parameters:

H : qutip.qobj

System Hamiltonian.

a_ops : list of qutip.qobj

List of system operators that couple to the environment.

spectra_cb : list of callback functions

List of callback functions that evaluate the noise power spectrum at a given frequency.

c_ops : list of qutip.qobj

List of system collapse operators.

use_secular : bool

Flag (True of False) that indicates if the secular approximation should be used.

sec_cutoff : float {0.1}

Threshold for secular approximation.

Returns:

R, kets: qutip.Qobj, list of qutip.Qobj

R is the Bloch-Redfield tensor and kets is a list eigenstates of the Hamiltonian.

bloch_redfield_solve(R, ekets, rho0, tlist, e_ops=[], options=None, progress_bar=None)[source]

Evolve the ODEs defined by Bloch-Redfield master equation. The Bloch-Redfield tensor can be calculated by the function bloch_redfield_tensor.

Parameters:

R : qutip.qobj

Bloch-Redfield tensor.

ekets : array of qutip.qobj

Array of kets that make up a basis tranformation for the eigenbasis.

rho0 : qutip.qobj

Initial density matrix.

tlist : list / array

List of times for \(t\).

e_ops : list of qutip.qobj / callback function

List of operators for which to evaluate expectation values.

options : qutip.Qdeoptions

Options for the ODE solver.

Returns:

output: qutip.solver

An instance of the class qutip.solver, which contains either an array of expectation values for the times specified by tlist.

Floquet States and Floquet-Markov Master Equation

fmmesolve(H, rho0, tlist, c_ops=[], e_ops=[], spectra_cb=[], T=None, args={}, options=<qutip.solver.Options object>, floquet_basis=True, kmax=5, _safe_mode=True)[source]

Solve the dynamics for the system using the Floquet-Markov master equation.

Note

This solver currently does not support multiple collapse operators.

Parameters:

H : qutip.qobj

system Hamiltonian.

rho0 / psi0 : qutip.qobj

initial density matrix or state vector (ket).

tlist : list / array

list of times for \(t\).

c_ops : list of qutip.qobj

list of collapse operators.

e_ops : list of qutip.qobj / callback function

list of operators for which to evaluate expectation values.

spectra_cb : list callback functions

List of callback functions that compute the noise power spectrum as a function of frequency for the collapse operators in c_ops.

T : float

The period of the time-dependence of the hamiltonian. The default value ‘None’ indicates that the ‘tlist’ spans a single period of the driving.

args : dictionary

dictionary of parameters for time-dependent Hamiltonians and collapse operators.

This dictionary should also contain an entry ‘w_th’, which is the temperature of the environment (if finite) in the energy/frequency units of the Hamiltonian. For example, if the Hamiltonian written in units of 2pi GHz, and the temperature is given in K, use the following conversion

>>> temperature = 25e-3 # unit K
>>> h = 6.626e-34
>>> kB = 1.38e-23
>>> args['w_th'] = temperature * (kB / h) * 2 * pi * 1e-9

options : qutip.solver

options for the ODE solver.

k_max : int

The truncation of the number of sidebands (default 5).

Returns:

output : qutip.solver

An instance of the class qutip.solver, which contains either an array of expectation values for the times specified by tlist.

floquet_modes(H, T, args=None, sort=False, U=None)[source]

Calculate the initial Floquet modes Phi_alpha(0) for a driven system with period T.

Returns a list of qutip.qobj instances representing the Floquet modes and a list of corresponding quasienergies, sorted by increasing quasienergy in the interval [-pi/T, pi/T]. The optional parameter sort decides if the output is to be sorted in increasing quasienergies or not.

Parameters:

H : qutip.qobj

system Hamiltonian, time-dependent with period T

args : dictionary

dictionary with variables required to evaluate H

T : float

The period of the time-dependence of the hamiltonian. The default value ‘None’ indicates that the ‘tlist’ spans a single period of the driving.

U : qutip.qobj

The propagator for the time-dependent Hamiltonian with period T. If U is None (default), it will be calculated from the Hamiltonian H using qutip.propagator.propagator.

Returns:

output : list of kets, list of quasi energies

Two lists: the Floquet modes as kets and the quasi energies.

floquet_modes_t(f_modes_0, f_energies, t, H, T, args=None)[source]

Calculate the Floquet modes at times tlist Phi_alpha(tlist) propagting the initial Floquet modes Phi_alpha(0)

Parameters:

f_modes_0 : list of qutip.qobj (kets)

Floquet modes at \(t\)

f_energies : list

Floquet energies.

t : float

The time at which to evaluate the floquet modes.

H : qutip.qobj

system Hamiltonian, time-dependent with period T

args : dictionary

dictionary with variables required to evaluate H

T : float

The period of the time-dependence of the hamiltonian.

Returns:

output : list of kets

The Floquet modes as kets at time \(t\)

floquet_modes_table(f_modes_0, f_energies, tlist, H, T, args=None)[source]

Pre-calculate the Floquet modes for a range of times spanning the floquet period. Can later be used as a table to look up the floquet modes for any time.

Parameters:

f_modes_0 : list of qutip.qobj (kets)

Floquet modes at \(t\)

f_energies : list

Floquet energies.

tlist : array

The list of times at which to evaluate the floquet modes.

H : qutip.qobj

system Hamiltonian, time-dependent with period T

T : float

The period of the time-dependence of the hamiltonian.

args : dictionary

dictionary with variables required to evaluate H

Returns:

output : nested list

A nested list of Floquet modes as kets for each time in tlist

floquet_modes_t_lookup(f_modes_table_t, t, T)[source]

Lookup the floquet mode at time t in the pre-calculated table of floquet modes in the first period of the time-dependence.

Parameters:

f_modes_table_t : nested list of qutip.qobj (kets)

A lookup-table of Floquet modes at times precalculated by qutip.floquet.floquet_modes_table.

t : float

The time for which to evaluate the Floquet modes.

T : float

The period of the time-dependence of the hamiltonian.

Returns:

output : nested list

A list of Floquet modes as kets for the time that most closely matching the time t in the supplied table of Floquet modes.

floquet_states_t(f_modes_0, f_energies, t, H, T, args=None)[source]

Evaluate the floquet states at time t given the initial Floquet modes.

Parameters:

f_modes_t : list of qutip.qobj (kets)

A list of initial Floquet modes (for time \(t=0\)).

f_energies : array

The Floquet energies.

t : float

The time for which to evaluate the Floquet states.

H : qutip.qobj

System Hamiltonian, time-dependent with period T.

T : float

The period of the time-dependence of the hamiltonian.

args : dictionary

Dictionary with variables required to evaluate H.

Returns:

output : list

A list of Floquet states for the time \(t\).

floquet_wavefunction_t(f_modes_0, f_energies, f_coeff, t, H, T, args=None)[source]

Evaluate the wavefunction for a time t using the Floquet state decompositon, given the initial Floquet modes.

Parameters:

f_modes_t : list of qutip.qobj (kets)

A list of initial Floquet modes (for time \(t=0\)).

f_energies : array

The Floquet energies.

f_coeff : array

The coefficients for Floquet decomposition of the initial wavefunction.

t : float

The time for which to evaluate the Floquet states.

H : qutip.qobj

System Hamiltonian, time-dependent with period T.

T : float

The period of the time-dependence of the hamiltonian.

args : dictionary

Dictionary with variables required to evaluate H.

Returns:

output : qutip.qobj

The wavefunction for the time \(t\).

floquet_state_decomposition(f_states, f_energies, psi)[source]

Decompose the wavefunction psi (typically an initial state) in terms of the Floquet states, \(\psi = \sum_\alpha c_\alpha \psi_\alpha(0)\).

Parameters:

f_states : list of qutip.qobj (kets)

A list of Floquet modes.

f_energies : array

The Floquet energies.

psi : qutip.qobj

The wavefunction to decompose in the Floquet state basis.

Returns:

output : array

The coefficients \(c_\alpha\) in the Floquet state decomposition.

fsesolve(H, psi0, tlist, e_ops=[], T=None, args={}, Tsteps=100)[source]

Solve the Schrodinger equation using the Floquet formalism.

Parameters:

H : qutip.qobj.Qobj

System Hamiltonian, time-dependent with period T.

psi0 : qutip.qobj

Initial state vector (ket).

tlist : list / array

list of times for \(t\).

e_ops : list of qutip.qobj / callback function

list of operators for which to evaluate expectation values. If this list is empty, the state vectors for each time in tlist will be returned instead of expectation values.

T : float

The period of the time-dependence of the hamiltonian.

args : dictionary

Dictionary with variables required to evaluate H.

Tsteps : integer

The number of time steps in one driving period for which to precalculate the Floquet modes. Tsteps should be an even number.

Returns:

output : qutip.solver.Result

An instance of the class qutip.solver.Result, which contains either an array of expectation values or an array of state vectors, for the times specified by tlist.

Stochastic Schrödinger Equation and Master Equation

This module contains functions for solving stochastic schrodinger and master equations. The API should not be considered stable, and is subject to change when we work more on optimizing this module for performance and features.

smesolve(H, rho0, times, c_ops=[], sc_ops=[], e_ops=[], _safe_mode=True, **kwargs)[source]

Solve stochastic master equation. Dispatch to specific solvers depending on the value of the solver keyword argument.

Parameters:

H : qutip.Qobj

System Hamiltonian.

rho0 : qutip.Qobj

Initial density matrix or state vector (ket).

times : list / array

List of times for \(t\). Must be uniformly spaced.

c_ops : list of qutip.Qobj

Deterministic collapse operator which will contribute with a standard Lindblad type of dissipation.

sc_ops : list of qutip.Qobj

List of stochastic collapse operators. Each stochastic collapse operator will give a deterministic and stochastic contribution to the eqaution of motion according to how the d1 and d2 functions are defined.

e_ops : list of qutip.Qobj / callback function single

single operator or list of operators for which to evaluate expectation values.

kwargs : dictionary

Optional keyword arguments. See qutip.stochastic.StochasticSolverOptions.

Returns:

output: qutip.solver.SolverResult

An instance of the class qutip.solver.SolverResult.

ssesolve(H, psi0, times, sc_ops=[], e_ops=[], _safe_mode=True, **kwargs)[source]

Solve the stochastic Schrödinger equation. Dispatch to specific solvers depending on the value of the solver keyword argument.

Parameters:

H : qutip.Qobj

System Hamiltonian.

psi0 : qutip.Qobj

Initial state vector (ket).

times : list / array

List of times for \(t\). Must be uniformly spaced.

sc_ops : list of qutip.Qobj

List of stochastic collapse operators. Each stochastic collapse operator will give a deterministic and stochastic contribution to the equation of motion according to how the d1 and d2 functions are defined.

e_ops : list of qutip.Qobj

Single operator or list of operators for which to evaluate expectation values.

kwargs : dictionary

Optional keyword arguments. See qutip.stochastic.StochasticSolverOptions.

Returns:

output: qutip.solver.SolverResult

An instance of the class qutip.solver.SolverResult.

smepdpsolve(H, rho0, times, c_ops, e_ops, **kwargs)[source]

A stochastic (piecewse deterministic process) PDP solver for density matrix evolution.

Parameters:

H : qutip.Qobj

System Hamiltonian.

rho0 : qutip.Qobj

Initial density matrix.

times : list / array

List of times for \(t\). Must be uniformly spaced.

c_ops : list of qutip.Qobj

Deterministic collapse operator which will contribute with a standard Lindblad type of dissipation.

sc_ops : list of qutip.Qobj

List of stochastic collapse operators. Each stochastic collapse operator will give a deterministic and stochastic contribution to the eqaution of motion according to how the d1 and d2 functions are defined.

e_ops : list of qutip.Qobj / callback function single

single operator or list of operators for which to evaluate expectation values.

kwargs : dictionary

Optional keyword arguments. See qutip.stochastic.StochasticSolverOptions.

Returns:

output: qutip.solver.SolverResult

An instance of the class qutip.solver.SolverResult.

ssepdpsolve(H, psi0, times, c_ops, e_ops, **kwargs)[source]

A stochastic (piecewse deterministic process) PDP solver for wavefunction evolution. For most purposes, use qutip.mcsolve instead for quantum trajectory simulations.

Parameters:

H : qutip.Qobj

System Hamiltonian.

psi0 : qutip.Qobj

Initial state vector (ket).

times : list / array

List of times for \(t\). Must be uniformly spaced.

c_ops : list of qutip.Qobj

Deterministic collapse operator which will contribute with a standard Lindblad type of dissipation.

e_ops : list of qutip.Qobj / callback function single

single operator or list of operators for which to evaluate expectation values.

kwargs : dictionary

Optional keyword arguments. See qutip.stochastic.StochasticSolverOptions.

Returns:

output: qutip.solver.SolverResult

An instance of the class qutip.solver.SolverResult.

Correlation Functions

correlation(H, state0, tlist, taulist, c_ops, a_op, b_op, solver='me', reverse=False, args={}, options=<qutip.solver.Options object>)[source]

Calculate the two-operator two-time correlation function: \(\left<A(t+\tau)B(t)\right>\) along two time axes using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

state0 : Qobj

Initial state density matrix \(\rho(t_0)\) or state vector \(\psi(t_0)\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

tlist : array_like

list of times for \(t\). tlist must be positive and contain the element 0. When taking steady-steady correlations only one tlist value is necessary, i.e. when \(t \rightarrow \infty\); here tlist is automatically set, ignoring user input.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

reverse : bool

If True, calculate \(\left<A(t)B(t+\tau)\right>\) instead of \(\left<A(t+\tau)B(t)\right>\).

solver : str

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_mat : array

An 2-dimensional array (matrix) of correlation values for the times specified by tlist (first index) and taulist (second index). If tlist is None, then a 1-dimensional array of correlation values is returned instead.

References

See, Gardiner, Quantum Noise, Section 5.2.

correlation_ss(H, taulist, c_ops, a_op, b_op, solver='me', reverse=False, args={}, options=<qutip.solver.Options object>)[source]

Calculate the two-operator two-time correlation function:

\[\lim_{t \to \infty} \left<A(t+\tau)B(t)\right>\]

along one time axis (given steady-state initial conditions) using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Parameters:

H : Qobj

system Hamiltonian.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

reverse : bool

If True, calculate \(\lim_{t \to \infty} \left<A(t)B(t+\tau)\right>\) instead of \(\lim_{t \to \infty} \left<A(t+\tau)B(t)\right>\).

solver : str

choice of solver (me for master-equation and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_vec : array

An array of correlation values for the times specified by tlist.

References

See, Gardiner, Quantum Noise, Section 5.2.

correlation_2op_1t(H, state0, taulist, c_ops, a_op, b_op, solver='me', reverse=False, args={}, options=<qutip.solver.Options object>)[source]

Calculate the two-operator two-time correlation function: \(\left<A(t+\tau)B(t)\right>\) along one time axis using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

state0 : Qobj

Initial state density matrix \(\rho(t_0)\) or state vector \(\psi(t_0)\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

reverse : bool {False, True}

If True, calculate \(\left<A(t)B(t+\tau)\right>\) instead of \(\left<A(t+\tau)B(t)\right>\).

solver : str {‘me’, ‘mc’, ‘es’}

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

Solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_vec : ndarray

An array of correlation values for the times specified by tlist.

References

See, Gardiner, Quantum Noise, Section 5.2.

correlation_2op_2t(H, state0, tlist, taulist, c_ops, a_op, b_op, solver='me', reverse=False, args={}, options=<qutip.solver.Options object>)[source]

Calculate the two-operator two-time correlation function: \(\left<A(t+\tau)B(t)\right>\) along two time axes using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

state0 : Qobj

Initial state density matrix \(\rho_0\) or state vector \(\psi_0\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

tlist : array_like

list of times for \(t\). tlist must be positive and contain the element 0. When taking steady-steady correlations only one tlist value is necessary, i.e. when \(t \rightarrow \infty\); here tlist is automatically set, ignoring user input.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

reverse : bool {False, True}

If True, calculate \(\left<A(t)B(t+\tau)\right>\) instead of \(\left<A(t+\tau)B(t)\right>\).

solver : str

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_mat : ndarray

An 2-dimensional array (matrix) of correlation values for the times specified by tlist (first index) and taulist (second index). If tlist is None, then a 1-dimensional array of correlation values is returned instead.

References

See, Gardiner, Quantum Noise, Section 5.2.

correlation_3op_1t(H, state0, taulist, c_ops, a_op, b_op, c_op, solver='me', args={}, options=<qutip.solver.Options object>)[source]

Calculate the three-operator two-time correlation function: \(\left<A(t)B(t+\tau)C(t)\right>\) along one time axis using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Note: it is not possibly to calculate a physically meaningful correlation of this form where \(\tau<0\).

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

rho0 : Qobj

Initial state density matrix \(\rho(t_0)\) or state vector \(\psi(t_0)\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

c_op : Qobj

operator C.

solver : str

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_vec : array

An array of correlation values for the times specified by taulist

References

See, Gardiner, Quantum Noise, Section 5.2.

correlation_3op_2t(H, state0, tlist, taulist, c_ops, a_op, b_op, c_op, solver='me', args={}, options=<qutip.solver.Options object>)[source]

Calculate the three-operator two-time correlation function: \(\left<A(t)B(t+\tau)C(t)\right>\) along two time axes using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Note: it is not possibly to calculate a physically meaningful correlation of this form where \(\tau<0\).

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

rho0 : Qobj

Initial state density matrix \(\rho_0\) or state vector \(\psi_0\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

tlist : array_like

list of times for \(t\). tlist must be positive and contain the element 0. When taking steady-steady correlations only one tlist value is necessary, i.e. when \(t \rightarrow \infty\); here tlist is automatically set, ignoring user input.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

c_op : Qobj

operator C.

solver : str

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_mat : array

An 2-dimensional array (matrix) of correlation values for the times specified by tlist (first index) and taulist (second index). If tlist is None, then a 1-dimensional array of correlation values is returned instead.

References

See, Gardiner, Quantum Noise, Section 5.2.

correlation_4op_1t(H, state0, taulist, c_ops, a_op, b_op, c_op, d_op, solver='me', args={}, options=<qutip.solver.Options object>)[source]

Calculate the four-operator two-time correlation function: \(\left<A(t)B(t+\tau)C(t+\tau)D(t)\right>\) along one time axis using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Note: it is not possibly to calculate a physically meaningful correlation of this form where \(\tau<0\).

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

rho0 : Qobj

Initial state density matrix \(\rho(t_0)\) or state vector \(\psi(t_0)\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

c_op : Qobj

operator C.

d_op : Qobj

operator D.

solver : str

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_vec : array

An array of correlation values for the times specified by taulist.

References

See, Gardiner, Quantum Noise, Section 5.2.

Note

Deprecated in QuTiP 3.1 Use correlation_3op_1t() instead.

correlation_4op_2t(H, state0, tlist, taulist, c_ops, a_op, b_op, c_op, d_op, solver='me', args={}, options=<qutip.solver.Options object>)[source]

Calculate the four-operator two-time correlation function: \(\left<A(t)B(t+\tau)C(t+\tau)D(t)\right>\) along two time axes using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Note: it is not possibly to calculate a physically meaningful correlation of this form where \(\tau<0\).

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

rho0 : Qobj

Initial state density matrix \(\rho_0\) or state vector \(\psi_0\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

tlist : array_like

list of times for \(t\). tlist must be positive and contain the element 0. When taking steady-steady correlations only one tlist value is necessary, i.e. when \(t \rightarrow \infty\); here tlist is automatically set, ignoring user input.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

c_op : Qobj

operator C.

d_op : Qobj

operator D.

solver : str

choice of solver (me for master-equation, mc for Monte Carlo, and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

corr_mat : array

An 2-dimensional array (matrix) of correlation values for the times specified by tlist (first index) and taulist (second index). If tlist is None, then a 1-dimensional array of correlation values is returned instead.

References

See, Gardiner, Quantum Noise, Section 5.2.

spectrum(H, wlist, c_ops, a_op, b_op, solver='es', use_pinv=False)[source]

Calculate the spectrum of the correlation function \(\lim_{t \to \infty} \left<A(t+\tau)B(t)\right>\), i.e., the Fourier transform of the correlation function:

\[S(\omega) = \int_{-\infty}^{\infty} \lim_{t \to \infty} \left<A(t+\tau)B(t)\right> e^{-i\omega\tau} d\tau.\]

using the solver indicated by the solver parameter. Note: this spectrum is only defined for stationary statistics (uses steady state rho0)

Parameters:

H : qutip.qobj

system Hamiltonian.

wlist : array_like

list of frequencies for \(\omega\).

c_ops : list

list of collapse operators.

a_op : Qobj

operator A.

b_op : Qobj

operator B.

solver : str

choice of solver (es for exponential series and pi for psuedo-inverse).

use_pinv : bool

For use with the pi solver: if True use numpy’s pinv method, otherwise use a generic solver.

Returns:

spectrum : array

An array with spectrum \(S(\omega)\) for the frequencies specified in wlist.

spectrum_ss(H, wlist, c_ops, a_op, b_op)[source]

Calculate the spectrum of the correlation function \(\lim_{t \to \infty} \left<A(t+\tau)B(t)\right>\), i.e., the Fourier transform of the correlation function:

\[S(\omega) = \int_{-\infty}^{\infty} \lim_{t \to \infty} \left<A(t+\tau)B(t)\right> e^{-i\omega\tau} d\tau.\]

using an eseries based solver Note: this spectrum is only defined for stationary statistics (uses steady state rho0).

Parameters:

H : qutip.qobj

system Hamiltonian.

wlist : array_like

list of frequencies for \(\omega\).

c_ops : list of qutip.qobj

list of collapse operators.

a_op : qutip.qobj

operator A.

b_op : qutip.qobj

operator B.

use_pinv : bool

If True use numpy’s pinv method, otherwise use a generic solver.

Returns:

spectrum : array

An array with spectrum \(S(\omega)\) for the frequencies specified in wlist.

spectrum_pi(H, wlist, c_ops, a_op, b_op, use_pinv=False)[source]

Calculate the spectrum of the correlation function \(\lim_{t \to \infty} \left<A(t+\tau)B(t)\right>\), i.e., the Fourier transform of the correlation function:

\[S(\omega) = \int_{-\infty}^{\infty} \lim_{t \to \infty} \left<A(t+\tau)B(t)\right> e^{-i\omega\tau} d\tau.\]

using a psuedo-inverse method. Note: this spectrum is only defined for stationary statistics (uses steady state rho0)

Parameters:

H : qutip.qobj

system Hamiltonian.

wlist : array_like

list of frequencies for \(\omega\).

c_ops : list of qutip.qobj

list of collapse operators.

a_op : qutip.qobj

operator A.

b_op : qutip.qobj

operator B.

use_pinv : bool

If True use numpy’s pinv method, otherwise use a generic solver.

Returns:

spectrum : array

An array with spectrum \(S(\omega)\) for the frequencies specified in wlist.

spectrum_correlation_fft(tlist, y)[source]

Calculate the power spectrum corresponding to a two-time correlation function using FFT.

Parameters:

tlist : array_like

list/array of times \(t\) which the correlation function is given.

y : array_like

list/array of correlations corresponding to time delays \(t\).

Returns:

w, S : tuple

Returns an array of angular frequencies ‘w’ and the corresponding one-sided power spectrum ‘S(w)’.

coherence_function_g1(H, state0, taulist, c_ops, a_op, solver='me', args={}, options=<qutip.solver.Options object>)[source]

Calculate the normalized first-order quantum coherence function:

\[g^{(1)}(\tau) = \frac{\langle A^\dagger(\tau)A(0)\rangle} {\sqrt{\langle A^\dagger(\tau)A(\tau)\rangle \langle A^\dagger(0)A(0)\rangle}}\]

using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

state0 : Qobj

Initial state density matrix \(\rho(t_0)\) or state vector \(\psi(t_0)\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

solver : str

choice of solver (me for master-equation and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

g1, G1 : tuple

The normalized and unnormalized second-order coherence function.

coherence_function_g2(H, state0, taulist, c_ops, a_op, solver='me', args={}, options=<qutip.solver.Options object>)[source]

Calculate the normalized second-order quantum coherence function:

\[ g^{(2)}(\tau) = \frac{\langle A^\dagger(0)A^\dagger(\tau)A(\tau)A(0)\rangle} {\langle A^\dagger(\tau)A(\tau)\rangle \langle A^\dagger(0)A(0)\rangle}\]

using the quantum regression theorem and the evolution solver indicated by the solver parameter.

Parameters:

H : Qobj

system Hamiltonian, may be time-dependent for solver choice of me or mc.

state0 : Qobj

Initial state density matrix \(\rho(t_0)\) or state vector \(\psi(t_0)\). If ‘state0’ is ‘None’, then the steady state will be used as the initial state. The ‘steady-state’ is only implemented for the me and es solvers.

taulist : array_like

list of times for \(\tau\). taulist must be positive and contain the element 0.

c_ops : list

list of collapse operators, may be time-dependent for solver choice of me or mc.

a_op : Qobj

operator A.

solver : str

choice of solver (me for master-equation and es for exponential series).

options : Options

solver options class. ntraj is taken as a two-element list because the mc correlator calls mcsolve() recursively; by default, ntraj=[20, 100]. mc_corr_eps prevents divide-by-zero errors in the mc correlator; by default, mc_corr_eps=1e-10.

Returns:

g2, G2 : tuple

The normalized and unnormalized second-order coherence function.

Steady-state Solvers

Module contains functions for solving for the steady state density matrix of open quantum systems defined by a Liouvillian or Hamiltonian and a list of collapse operators.

steadystate(A, c_op_list=[], **kwargs)[source]

Calculates the steady state for quantum evolution subject to the supplied Hamiltonian or Liouvillian operator and (if given a Hamiltonian) a list of collapse operators.

If the user passes a Hamiltonian then it, along with the list of collapse operators, will be converted into a Liouvillian operator in Lindblad form.

Parameters:

A : qobj

A Hamiltonian or Liouvillian operator.

c_op_list : list

A list of collapse operators.

method : str {‘direct’, ‘eigen’, ‘iterative-gmres’,

‘iterative-lgmres’, ‘iterative-bicgstab’, ‘svd’, ‘power’, ‘power-gmres’, ‘power-lgmres’, ‘power-bicgstab’}

Method for solving the underlying linear equation. Direct LU solver ‘direct’ (default), sparse eigenvalue problem ‘eigen’, iterative GMRES method ‘iterative-gmres’, iterative LGMRES method ‘iterative-lgmres’, iterative BICGSTAB method ‘iterative-bicgstab’, SVD ‘svd’ (dense), or inverse-power method ‘power’. The iterative power methods ‘power-gmres’, ‘power-lgmres’, ‘power-bicgstab’ use the same solvers as their direct counterparts.

return_info : bool, optional, default = False

Return a dictionary of solver-specific infomation about the solution and how it was obtained.

sparse : bool, optional, default = True

Solve for the steady state using sparse algorithms. If set to False, the underlying Liouvillian operator will be converted into a dense matrix. Use only for ‘smaller’ systems.

use_rcm : bool, optional, default = False

Use reverse Cuthill-Mckee reordering to minimize fill-in in the LU factorization of the Liouvillian.

use_wbm : bool, optional, default = False

Use Weighted Bipartite Matching reordering to make the Liouvillian diagonally dominant. This is useful for iterative preconditioners only, and is set to True by default when finding a preconditioner.

weight : float, optional

Sets the size of the elements used for adding the unity trace condition to the linear solvers. This is set to the average abs value of the Liouvillian elements if not specified by the user.

x0 : ndarray, optional

ITERATIVE ONLY. Initial guess for solution vector.

maxiter : int, optional, default=1000

ITERATIVE ONLY. Maximum number of iterations to perform.

tol : float, optional, default=1e-12

ITERATIVE ONLY. Tolerance used for terminating solver.

permc_spec : str, optional, default=’COLAMD’

ITERATIVE ONLY. Column ordering used internally by superLU for the ‘direct’ LU decomposition method. Options include ‘COLAMD’ and ‘NATURAL’. If using RCM then this is set to ‘NATURAL’ automatically unless explicitly specified.

use_precond : bool optional, default = False

ITERATIVE ONLY. Use an incomplete sparse LU decomposition as a preconditioner for the ‘iterative’ GMRES and BICG solvers. Speeds up convergence time by orders of magnitude in many cases.

M : {sparse matrix, dense matrix, LinearOperator}, optional

ITERATIVE ONLY. Preconditioner for A. The preconditioner should approximate the inverse of A. Effective preconditioning can dramatically improve the rate of convergence for iterative methods. If no preconditioner is given and use_precond = True, then one is generated automatically.

fill_factor : float, optional, default = 100

ITERATIVE ONLY. Specifies the fill ratio upper bound (>=1) of the iLU preconditioner. Lower values save memory at the cost of longer execution times and a possible singular factorization.

drop_tol : float, optional, default = 1e-4

ITERATIVE ONLY. Sets the threshold for the magnitude of preconditioner elements that should be dropped. Can be reduced for a courser factorization at the cost of an increased number of iterations, and a possible singular factorization.

diag_pivot_thresh : float, optional, default = None

ITERATIVE ONLY. Sets the threshold between [0,1] for which diagonal elements are considered acceptable pivot points when using a preconditioner. A value of zero forces the pivot to be the diagonal element.

ILU_MILU : str, optional, default = ‘smilu_2’

ITERATIVE ONLY. Selects the incomplete LU decomposition method algoithm used in creating the preconditoner. Should only be used by advanced users.

Returns:

dm : qobj

Steady state density matrix.

info : dict, optional

Dictionary containing solver-specific information about the solution.

Notes

The SVD method works only for dense operators (i.e. small systems).

build_preconditioner(A, c_op_list=[], **kwargs)[source]

Constructs a iLU preconditioner necessary for solving for the steady state density matrix using the iterative linear solvers in the ‘steadystate’ function.

Parameters:

A : qobj

A Hamiltonian or Liouvillian operator.

c_op_list : list

A list of collapse operators.

return_info : bool, optional, default = False

Return a dictionary of solver-specific infomation about the solution and how it was obtained.

use_rcm : bool, optional, default = False

Use reverse Cuthill-Mckee reordering to minimize fill-in in the LU factorization of the Liouvillian.

use_wbm : bool, optional, default = False

Use Weighted Bipartite Matching reordering to make the Liouvillian diagonally dominant. This is useful for iterative preconditioners only, and is set to True by default when finding a preconditioner.

weight : float, optional

Sets the size of the elements used for adding the unity trace condition to the linear solvers. This is set to the average abs value of the Liouvillian elements if not specified by the user.

method : str, default = ‘iterative’

Tells the preconditioner what type of Liouvillian to build for iLU factorization. For direct iterative methods use ‘iterative’. For power iterative methods use ‘power’.

permc_spec : str, optional, default=’COLAMD’

Column ordering used internally by superLU for the ‘direct’ LU decomposition method. Options include ‘COLAMD’ and ‘NATURAL’. If using RCM then this is set to ‘NATURAL’ automatically unless explicitly specified.

fill_factor : float, optional, default = 100

Specifies the fill ratio upper bound (>=1) of the iLU preconditioner. Lower values save memory at the cost of longer execution times and a possible singular factorization.

drop_tol : float, optional, default = 1e-4

Sets the threshold for the magnitude of preconditioner elements that should be dropped. Can be reduced for a courser factorization at the cost of an increased number of iterations, and a possible singular factorization.

diag_pivot_thresh : float, optional, default = None

Sets the threshold between [0,1] for which diagonal elements are considered acceptable pivot points when using a preconditioner. A value of zero forces the pivot to be the diagonal element.

ILU_MILU : str, optional, default = ‘smilu_2’

Selects the incomplete LU decomposition method algoithm used in creating the preconditoner. Should only be used by advanced users.

Returns:

lu : object

Returns a SuperLU object representing iLU preconditioner.

info : dict, optional

Dictionary containing solver-specific information.

Propagators

propagator(H, t, c_op_list=[], args={}, options=None, unitary_mode='batch', parallel=False, progress_bar=None, **kwargs)[source]

Calculate the propagator U(t) for the density matrix or wave function such that \(\psi(t) = U(t)\psi(0)\) or \(\rho_{\mathrm vec}(t) = U(t) \rho_{\mathrm vec}(0)\) where \(\rho_{\mathrm vec}\) is the vector representation of the density matrix.

Parameters:

H : qobj or list

Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in qutip.mesolve).

t : float or array-like

Time or list of times for which to evaluate the propagator.

c_op_list : list

List of qobj collapse operators.

args : list/array/dictionary

Parameters to callback functions for time-dependent Hamiltonians and collapse operators.

options : qutip.Options

with options for the ODE solver.

unitary_mode = str (‘batch’, ‘single’)

Solve all basis vectors simulaneously (‘batch’) or individually (‘single’).

parallel : bool {False, True}

Run the propagator in parallel mode. This will override the unitary_mode settings if set to True.

progress_bar: BaseProgressBar

Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used.

Returns:

a : qobj

Instance representing the propagator \(U(t)\).

propagator_steadystate(U)[source]

Find the steady state for successive applications of the propagator \(U\).

Parameters:

U : qobj

Operator representing the propagator.

Returns:

a : qobj

Instance representing the steady-state density matrix.

Time-dependent problems

rhs_generate(H, c_ops, args={}, options=<qutip.solver.Options object>, name=None, cleanup=True)[source]

Generates the Cython functions needed for solving the dynamics of a given system using the mesolve function inside a parfor loop.

Parameters:

H : qobj

System Hamiltonian.

c_ops : list

list of collapse operators.

args : dict

Arguments for time-dependent Hamiltonian and collapse operator terms.

options : Options

Instance of ODE solver options.

name: str

Name of generated RHS

cleanup: bool

Whether the generated cython file should be automatically removed or not.

Notes

Using this function with any solver other than the mesolve function will result in an error.

rhs_clear()[source]

Resets the string-format time-dependent Hamiltonian parameters.

Returns:Nothing, just clears data from internal config module.

Visualization

Pseudoprobability Functions

qfunc(state, xvec, yvec, g=1.4142135623730951)[source]

Q-function of a given state vector or density matrix at points xvec + i * yvec.

Parameters:

state : qobj

A state vector or density matrix.

xvec : array_like

x-coordinates at which to calculate the Wigner function.

yvec : array_like

y-coordinates at which to calculate the Wigner function.

g : float

Scaling factor for a = 0.5 * g * (x + iy), default g = sqrt(2).

Returns:

Q : array

Values representing the Q-function calculated over the specified range [xvec,yvec].

spin_q_function(rho, theta, phi)[source]

Husimi Q-function for spins.

Parameters:

state : qobj

A state vector or density matrix for a spin-j quantum system.

theta : array_like

theta-coordinates at which to calculate the Q function.

phi : array_like

phi-coordinates at which to calculate the Q function.

Returns:

Q, THETA, PHI : 2d-array

Values representing the spin Q function at the values specified by THETA and PHI.

spin_wigner(rho, theta, phi)[source]

Wigner function for spins on the Bloch sphere.

Parameters:

state : qobj

A state vector or density matrix for a spin-j quantum system.

theta : array_like

theta-coordinates at which to calculate the Q function.

phi : array_like

phi-coordinates at which to calculate the Q function.

Returns:

W, THETA, PHI : 2d-array

Values representing the spin Wigner function at the values specified by THETA and PHI.

Notes

Experimental.

wigner(psi, xvec, yvec, method='clenshaw', g=1.4142135623730951, sparse=False, parfor=False)[source]

Wigner function for a state vector or density matrix at points xvec + i * yvec.

Parameters:

state : qobj

A state vector or density matrix.

xvec : array_like

x-coordinates at which to calculate the Wigner function.

yvec : array_like

y-coordinates at which to calculate the Wigner function. Does not apply to the ‘fft’ method.

g : float

Scaling factor for a = 0.5 * g * (x + iy), default g = sqrt(2).

method : string {‘clenshaw’, ‘iterative’, ‘laguerre’, ‘fft’}

Select method ‘clenshaw’ ‘iterative’, ‘laguerre’, or ‘fft’, where ‘clenshaw’ and ‘iterative’ use an iterative method to evaluate the Wigner functions for density matrices \(|m><n|\), while ‘laguerre’ uses the Laguerre polynomials in scipy for the same task. The ‘fft’ method evaluates the Fourier transform of the density matrix. The ‘iterative’ method is default, and in general recommended, but the ‘laguerre’ method is more efficient for very sparse density matrices (e.g., superpositions of Fock states in a large Hilbert space). The ‘clenshaw’ method is the preferred method for dealing with density matrices that have a large number of excitations (>~50). ‘clenshaw’ is a fast and numerically stable method.

sparse : bool {False, True}

Tells the default solver whether or not to keep the input density matrix in sparse format. As the dimensions of the density matrix grow, setthing this flag can result in increased performance.

parfor : bool {False, True}

Flag for calculating the Laguerre polynomial based Wigner function method=’laguerre’ in parallel using the parfor function.

Returns:

W : array

Values representing the Wigner function calculated over the specified range [xvec,yvec].

yvex : array

FFT ONLY. Returns the y-coordinate values calculated via the Fourier transform.

Notes

The ‘fft’ method accepts only an xvec input for the x-coordinate. The y-coordinates are calculated internally.

References

Ulf Leonhardt, Measuring the Quantum State of Light, (Cambridge University Press, 1997)

Graphs and Visualization

Functions for visualizing results of quantum dynamics simulations, visualizations of quantum states and processes.

hinton(rho, xlabels=None, ylabels=None, title=None, ax=None, cmap=None, label_top=True)[source]

Draws a Hinton diagram for visualizing a density matrix or superoperator.

Parameters:

rho : qobj

Input density matrix or superoperator.

xlabels : list of strings or False

list of x labels

ylabels : list of strings or False

list of y labels

title : string

title of the plot (optional)

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

cmap : a matplotlib colormap instance

Color map to use when plotting.

label_top : bool

If True, x-axis labels will be placed on top, otherwise they will appear below the plot.

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

Raises:

ValueError

Input argument is not a quantum object.

matrix_histogram(M, xlabels=None, ylabels=None, title=None, limits=None, colorbar=True, fig=None, ax=None)[source]

Draw a histogram for the matrix M, with the given x and y labels and title.

Parameters:

M : Matrix of Qobj

The matrix to visualize

xlabels : list of strings

list of x labels

ylabels : list of strings

list of y labels

title : string

title of the plot (optional)

limits : list/array with two float numbers

The z-axis limits [min, max] (optional)

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

Raises:

ValueError

Input argument is not valid.

matrix_histogram_complex(M, xlabels=None, ylabels=None, title=None, limits=None, phase_limits=None, colorbar=True, fig=None, ax=None, threshold=None)[source]

Draw a histogram for the amplitudes of matrix M, using the argument of each element for coloring the bars, with the given x and y labels and title.

Parameters:

M : Matrix of Qobj

The matrix to visualize

xlabels : list of strings

list of x labels

ylabels : list of strings

list of y labels

title : string

title of the plot (optional)

limits : list/array with two float numbers

The z-axis limits [min, max] (optional)

phase_limits : list/array with two float numbers

The phase-axis (colorbar) limits [min, max] (optional)

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

threshold: float (None)

Threshold for when bars of smaller height should be transparent. If not set, all bars are colored according to the color map.

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

Raises:

ValueError

Input argument is not valid.

plot_energy_levels(H_list, N=0, labels=None, show_ylabels=False, figsize=(8, 12), fig=None, ax=None)[source]

Plot the energy level diagrams for a list of Hamiltonians. Include up to N energy levels. For each element in H_list, the energy levels diagram for the cummulative Hamiltonian sum(H_list[0:n]) is plotted, where n is the index of an element in H_list.

Parameters:

H_list : List of Qobj

A list of Hamiltonians.

labels : List of string

A list of labels for each Hamiltonian

show_ylabels : Bool (default False)

Show y labels to the left of energy levels of the initial Hamiltonian.

N : int

The number of energy levels to plot

figsize : tuple (int,int)

The size of the figure (width, height).

fig : a matplotlib Figure instance

The Figure canvas in which the plot will be drawn.

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

Raises:

ValueError

Input argument is not valid.

plot_fock_distribution(rho, offset=0, fig=None, ax=None, figsize=(8, 6), title=None, unit_y_range=True)[source]

Plot the Fock distribution for a density matrix (or ket) that describes an oscillator mode.

Parameters:

rho : qutip.qobj.Qobj

The density matrix (or ket) of the state to visualize.

fig : a matplotlib Figure instance

The Figure canvas in which the plot will be drawn.

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

title : string

An optional title for the figure.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_wigner_fock_distribution(rho, fig=None, axes=None, figsize=(8, 4), cmap=None, alpha_max=7.5, colorbar=False, method='iterative', projection='2d')[source]

Plot the Fock distribution and the Wigner function for a density matrix (or ket) that describes an oscillator mode.

Parameters:

rho : qutip.qobj.Qobj

The density matrix (or ket) of the state to visualize.

fig : a matplotlib Figure instance

The Figure canvas in which the plot will be drawn.

axes : a list of two matplotlib axes instances

The axes context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

cmap : a matplotlib cmap instance

The colormap.

alpha_max : float

The span of the x and y coordinates (both [-alpha_max, alpha_max]).

colorbar : bool

Whether (True) or not (False) a colorbar should be attached to the Wigner function graph.

method : string {‘iterative’, ‘laguerre’, ‘fft’}

The method used for calculating the wigner function. See the documentation for qutip.wigner for details.

projection: string {‘2d’, ‘3d’}

Specify whether the Wigner function is to be plotted as a contour graph (‘2d’) or surface plot (‘3d’).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_wigner(rho, fig=None, ax=None, figsize=(8, 4), cmap=None, alpha_max=7.5, colorbar=False, method='iterative', projection='2d')[source]

Plot the the Wigner function for a density matrix (or ket) that describes an oscillator mode.

Parameters:

rho : qutip.qobj.Qobj

The density matrix (or ket) of the state to visualize.

fig : a matplotlib Figure instance

The Figure canvas in which the plot will be drawn.

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

cmap : a matplotlib cmap instance

The colormap.

alpha_max : float

The span of the x and y coordinates (both [-alpha_max, alpha_max]).

colorbar : bool

Whether (True) or not (False) a colorbar should be attached to the Wigner function graph.

method : string {‘iterative’, ‘laguerre’, ‘fft’}

The method used for calculating the wigner function. See the documentation for qutip.wigner for details.

projection: string {‘2d’, ‘3d’}

Specify whether the Wigner function is to be plotted as a contour graph (‘2d’) or surface plot (‘3d’).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

sphereplot(theta, phi, values, fig=None, ax=None, save=False)[source]

Plots a matrix of values on a sphere

Parameters:

theta : float

Angle with respect to z-axis

phi : float

Angle in x-y plane

values : array

Data set to be plotted

fig : a matplotlib Figure instance

The Figure canvas in which the plot will be drawn.

ax : a matplotlib axes instance

The axes context in which the plot will be drawn.

save : bool {False , True}

Whether to save the figure or not

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_schmidt(ket, splitting=None, labels_iteration=(3, 2), theme='light', fig=None, ax=None, figsize=(6, 6))[source]

Plotting scheme related to Schmidt decomposition. Converts a state into a matrix (A_ij -> A_i^j), where rows are first particles and columns - last.

See also: plot_qubism with how=’before_after’ for a similar plot.

Parameters:

ket : Qobj

Pure state for plotting.

splitting : int

Plot for a number of first particles versus the rest. If not given, it is (number of particles + 1) // 2.

theme : ‘light’ (default) or ‘dark’

Set coloring theme for mapping complex values into colors. See: complex_array_to_rgb.

labels_iteration : int or pair of ints (default (3,2))

Number of particles to be shown as tick labels, for first (vertical) and last (horizontal) particles, respectively.

fig : a matplotlib figure instance

The figure canvas on which the plot will be drawn.

ax : a matplotlib axis instance

The axis context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_qubism(ket, theme='light', how='pairs', grid_iteration=1, legend_iteration=0, fig=None, ax=None, figsize=(6, 6))[source]

Qubism plot for pure states of many qudits. Works best for spin chains, especially with even number of particles of the same dimension. Allows to see entanglement between first 2*k particles and the rest.

More information:

J. Rodriguez-Laguna, P. Migdal, M. Ibanez Berganza, M. Lewenstein, G. Sierra, “Qubism: self-similar visualization of many-body wavefunctions”, New J. Phys. 14 053028 (2012), arXiv:1112.3560, http://dx.doi.org/10.1088/1367-2630/14/5/053028 (open access)
Parameters:

ket : Qobj

Pure state for plotting.

theme : ‘light’ (default) or ‘dark’

Set coloring theme for mapping complex values into colors. See: complex_array_to_rgb.

how : ‘pairs’ (default), ‘pairs_skewed’ or ‘before_after’

Type of Qubism plotting. Options:

‘pairs’ - typical coordinates, ‘pairs_skewed’ - for ferromagnetic/antriferromagnetic plots, ‘before_after’ - related to Schmidt plot (see also: plot_schmidt).

grid_iteration : int (default 1)

Helper lines to be drawn on plot. Show tiles for 2*grid_iteration particles vs all others.

legend_iteration : int (default 0) or ‘grid_iteration’ or ‘all’

Show labels for first 2*legend_iteration particles. Option ‘grid_iteration’ sets the same number of particles

as for grid_iteration.

Option ‘all’ makes label for all particles. Typically it should be 0, 1, 2 or perhaps 3.

fig : a matplotlib figure instance

The figure canvas on which the plot will be drawn.

ax : a matplotlib axis instance

The axis context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_expectation_values(results, ylabels=[], title=None, show_legend=False, fig=None, axes=None, figsize=(8, 4))[source]

Visualize the results (expectation values) for an evolution solver. results is assumed to be an instance of Result, or a list of Result instances.

Parameters:

results : (list of) qutip.solver.Result

List of results objects returned by any of the QuTiP evolution solvers.

ylabels : list of strings

The y-axis labels. List should be of the same length as results.

title : string

The title of the figure.

show_legend : bool

Whether or not to show the legend.

fig : a matplotlib Figure instance

The Figure canvas in which the plot will be drawn.

axes : a matplotlib axes instance

The axes context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_spin_distribution_2d(P, THETA, PHI, fig=None, ax=None, figsize=(8, 8))[source]

Plot a spin distribution function (given as meshgrid data) with a 2D projection where the surface of the unit sphere is mapped on the unit disk.

Parameters:

P : matrix

Distribution values as a meshgrid matrix.

THETA : matrix

Meshgrid matrix for the theta coordinate.

PHI : matrix

Meshgrid matrix for the phi coordinate.

fig : a matplotlib figure instance

The figure canvas on which the plot will be drawn.

ax : a matplotlib axis instance

The axis context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

plot_spin_distribution_3d(P, THETA, PHI, fig=None, ax=None, figsize=(8, 6))[source]

Plots a matrix of values on a sphere

Parameters:

P : matrix

Distribution values as a meshgrid matrix.

THETA : matrix

Meshgrid matrix for the theta coordinate.

PHI : matrix

Meshgrid matrix for the phi coordinate.

fig : a matplotlib figure instance

The figure canvas on which the plot will be drawn.

ax : a matplotlib axis instance

The axis context in which the plot will be drawn.

figsize : (width, height)

The size of the matplotlib figure (in inches) if it is to be created (that is, if no ‘fig’ and ‘ax’ arguments are passed).

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

orbital(theta, phi, *args)[source]

Calculates an angular wave function on a sphere. psi = orbital(theta,phi,ket1,ket2,...) calculates the angular wave function on a sphere at the mesh of points defined by theta and phi which is \(\sum_{lm} c_{lm} Y_{lm}(theta,phi)\) where \(C_{lm}\) are the coefficients specified by the list of kets. Each ket has 2l+1 components for some integer l.

Parameters:

theta : list/array

Polar angles

phi : list/array

Azimuthal angles

args : list/array

list of ket vectors.

Returns:

array for angular wave function

Quantum Process Tomography

qpt(U, op_basis_list)[source]

Calculate the quantum process tomography chi matrix for a given (possibly nonunitary) transformation matrix U, which transforms a density matrix in vector form according to:

vec(rho) = U * vec(rho0)

or

rho = vec2mat(U * mat2vec(rho0))

U can be calculated for an open quantum system using the QuTiP propagator function.

Parameters:

U : Qobj

Transformation operator. Can be calculated using QuTiP propagator function.

op_basis_list : list

A list of Qobj’s representing the basis states.

Returns:

chi : array

QPT chi matrix

qpt_plot(chi, lbls_list, title=None, fig=None, axes=None)[source]

Visualize the quantum process tomography chi matrix. Plot the real and imaginary parts separately.

Parameters:

chi : array

Input QPT chi matrix.

lbls_list : list

List of labels for QPT plot axes.

title : string

Plot title.

fig : figure instance

User defined figure instance used for generating QPT plot.

axes : list of figure axis instance

User defined figure axis instance (list of two axes) used for generating QPT plot.

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

qpt_plot_combined(chi, lbls_list, title=None, fig=None, ax=None, figsize=(8, 6), threshold=None)[source]

Visualize the quantum process tomography chi matrix. Plot bars with height and color corresponding to the absolute value and phase, respectively.

Parameters:

chi : array

Input QPT chi matrix.

lbls_list : list

List of labels for QPT plot axes.

title : string

Plot title.

fig : figure instance

User defined figure instance used for generating QPT plot.

ax : figure axis instance

User defined figure axis instance used for generating QPT plot (alternative to the fig argument).

threshold: float (None)

Threshold for when bars of smaller height should be transparent. If not set, all bars are colored according to the color map.

Returns:

fig, ax : tuple

A tuple of the matplotlib figure and axes instances used to produce the figure.

Quantum Information Processing

Gates

rx(phi, N=None, target=0)[source]

Single-qubit rotation for operator sigmax with angle phi.

Returns:

result : qobj

Quantum object for operator describing the rotation.

ry(phi, N=None, target=0)[source]

Single-qubit rotation for operator sigmay with angle phi.

Returns:

result : qobj

Quantum object for operator describing the rotation.

rz(phi, N=None, target=0)[source]

Single-qubit rotation for operator sigmaz with angle phi.

Returns:

result : qobj

Quantum object for operator describing the rotation.

sqrtnot(N=None, target=0)[source]

Single-qubit square root NOT gate.

Returns:

result : qobj

Quantum object for operator describing the square root NOT gate.

snot(N=None, target=0)[source]

Quantum object representing the SNOT (Hadamard) gate.

Returns:

snot_gate : qobj

Quantum object representation of SNOT gate.

Examples

>>> snot()
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = True
Qobj data =
[[ 0.70710678+0.j  0.70710678+0.j]
 [ 0.70710678+0.j -0.70710678+0.j]]
phasegate(theta, N=None, target=0)[source]

Returns quantum object representing the phase shift gate.

Parameters:

theta : float

Phase rotation angle.

Returns:

phase_gate : qobj

Quantum object representation of phase shift gate.

Examples

>>> phasegate(pi/4)
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = False
Qobj data =
[[ 1.00000000+0.j          0.00000000+0.j        ]
 [ 0.00000000+0.j          0.70710678+0.70710678j]]
cphase(theta, N=2, control=0, target=1)[source]

Returns quantum object representing the controlled phase shift gate.

Parameters:

theta : float

Phase rotation angle.

N : integer

The number of qubits in the target space.

control : integer

The index of the control qubit.

target : integer

The index of the target qubit.

Returns:

U : qobj

Quantum object representation of controlled phase gate.

cnot(N=None, control=0, target=1)[source]

Quantum object representing the CNOT gate.

Returns:

cnot_gate : qobj

Quantum object representation of CNOT gate

Examples

>>> cnot()
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
    [[ 1.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  1.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  1.+0.j]
     [ 0.+0.j  0.+0.j  1.+0.j  0.+0.j]]
csign(N=None, control=0, target=1)[source]

Quantum object representing the CSIGN gate.

Returns:

csign_gate : qobj

Quantum object representation of CSIGN gate

Examples

>>> csign()
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
    [[ 1.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  1.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  1.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  -1.+0.j]]
berkeley(N=None, targets=[0, 1])[source]

Quantum object representing the Berkeley gate.

Returns:

berkeley_gate : qobj

Quantum object representation of Berkeley gate

Examples

>>> berkeley()
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
    [[ cos(pi/8).+0.j  0.+0.j           0.+0.j           0.+sin(pi/8).j]
     [ 0.+0.j          cos(3pi/8).+0.j  0.+sin(3pi/8).j  0.+0.j]
     [ 0.+0.j          0.+sin(3pi/8).j  cos(3pi/8).+0.j  0.+0.j]
     [ 0.+sin(pi/8).j  0.+0.j           0.+0.j           cos(pi/8).+0.j]]
swapalpha(alpha, N=None, targets=[0, 1])[source]

Quantum object representing the SWAPalpha gate.

Returns:

swapalpha_gate : qobj

Quantum object representation of SWAPalpha gate

Examples

>>> swapalpha(alpha)
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 1.+0.j  0.+0.j                    0.+0.j                    0.+0.j]
 [ 0.+0.j  0.5*(1 + exp(j*pi*alpha)  0.5*(1 - exp(j*pi*alpha)  0.+0.j]
 [ 0.+0.j  0.5*(1 - exp(j*pi*alpha)  0.5*(1 + exp(j*pi*alpha)  0.+0.j]
 [ 0.+0.j  0.+0.j                    0.+0.j                    1.+0.j]]
swap(N=None, targets=[0, 1])[source]

Quantum object representing the SWAP gate.

Returns:

swap_gate : qobj

Quantum object representation of SWAP gate

Examples

>>> swap()
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 1.+0.j  0.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  1.+0.j  0.+0.j]
 [ 0.+0.j  1.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  0.+0.j  1.+0.j]]
iswap(N=None, targets=[0, 1])[source]

Quantum object representing the iSWAP gate.

Returns:

iswap_gate : qobj

Quantum object representation of iSWAP gate

Examples

>>> iswap()
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 1.+0.j  0.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  0.+1.j  0.+0.j]
 [ 0.+0.j  0.+1.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.+0.j  0.+0.j  1.+0.j]]
sqrtswap(N=None, targets=[0, 1])[source]

Quantum object representing the square root SWAP gate.

Returns:

sqrtswap_gate : qobj

Quantum object representation of square root SWAP gate

sqrtiswap(N=None, targets=[0, 1])[source]

Quantum object representing the square root iSWAP gate.

Returns:

sqrtiswap_gate : qobj

Quantum object representation of square root iSWAP gate

Examples

>>> sqrtiswap()
Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 1.00000000+0.j   0.00000000+0.j          0.00000000+0.j          0.00000000+0.j]
 [ 0.00000000+0.j   0.70710678+0.j          0.00000000-0.70710678j  0.00000000+0.j]
 [ 0.00000000+0.j   0.00000000-0.70710678j       0.70710678+0.j          0.00000000+0.j]
 [ 0.00000000+0.j   0.00000000+0.j          0.00000000+0.j          1.00000000+0.j]]
fredkin(N=None, control=0, targets=[1, 2])[source]

Quantum object representing the Fredkin gate.

Returns:

fredkin_gate : qobj

Quantum object representation of Fredkin gate.

Examples

>>> fredkin()
Quantum object: dims = [[2, 2, 2], [2, 2, 2]], shape = [8, 8], type = oper, isHerm = True
Qobj data =
    [[ 1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j]]
toffoli(N=None, controls=[0, 1], target=2)[source]

Quantum object representing the Toffoli gate.

Returns:

toff_gate : qobj

Quantum object representation of Toffoli gate.

Examples

>>> toffoli()
Quantum object: dims = [[2, 2, 2], [2, 2, 2]], shape = [8, 8], type = oper, isHerm = True
Qobj data =
    [[ 1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j  0.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j]
     [ 0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+0.j  1.+0.j  0.+0.j]]
rotation(op, phi, N=None, target=0)[source]

Single-qubit rotation for operator op with angle phi.

Returns:

result : qobj

Quantum object for operator describing the rotation.

controlled_gate(U, N=2, control=0, target=1, control_value=1)[source]

Create an N-qubit controlled gate from a single-qubit gate U with the given control and target qubits.

Parameters:

U : Qobj

Arbitrary single-qubit gate.

N : integer

The number of qubits in the target space.

control : integer

The index of the first control qubit.

target : integer

The index of the target qubit.

control_value : integer (1)

The state of the control qubit that activates the gate U.

Returns:

result : qobj

Quantum object representing the controlled-U gate.

globalphase(theta, N=1)[source]

Returns quantum object representing the global phase shift gate.

Parameters:

theta : float

Phase rotation angle.

Returns:

phase_gate : qobj

Quantum object representation of global phase shift gate.

Examples

>>> phasegate(pi/4)
Quantum object: dims = [[2], [2]], shape = [2, 2], type = oper, isHerm = False
Qobj data =
[[ 0.70710678+0.70710678j          0.00000000+0.j]
 [ 0.00000000+0.j          0.70710678+0.70710678j]]
hadamard_transform(N=1)[source]

Quantum object representing the N-qubit Hadamard gate.

Returns:

q : qobj

Quantum object representation of the N-qubit Hadamard gate.

gate_sequence_product(U_list, left_to_right=True)[source]

Calculate the overall unitary matrix for a given list of unitary operations

Parameters:

U_list : list

List of gates implementing the quantum circuit.

left_to_right : Boolean

Check if multiplication is to be done from left to right.

Returns:

U_overall : qobj

Overall unitary matrix of a given quantum circuit.

gate_expand_1toN(U, N, target)[source]

Create a Qobj representing a one-qubit gate that act on a system with N qubits.

Parameters:

U : Qobj

The one-qubit gate

N : integer

The number of qubits in the target space.

target : integer

The index of the target qubit.

Returns:

gate : qobj

Quantum object representation of N-qubit gate.

gate_expand_2toN(U, N, control=None, target=None, targets=None)[source]

Create a Qobj representing a two-qubit gate that act on a system with N qubits.

Parameters:

U : Qobj

The two-qubit gate

N : integer

The number of qubits in the target space.

control : integer

The index of the control qubit.

target : integer

The index of the target qubit.

targets : list

List of target qubits.

Returns:

gate : qobj

Quantum object representation of N-qubit gate.

gate_expand_3toN(U, N, controls=[0, 1], target=2)[source]

Create a Qobj representing a three-qubit gate that act on a system with N qubits.

Parameters:

U : Qobj

The three-qubit gate

N : integer

The number of qubits in the target space.

controls : list

The list of the control qubits.

target : integer

The index of the target qubit.

Returns:

gate : qobj

Quantum object representation of N-qubit gate.

Qubits

qubit_states(N=1, states=[0])[source]

Function to define initial state of the qubits.

Parameters:

N : Integer

Number of qubits in the register.

states : List

Initial state of each qubit.

Returns:

qstates : Qobj

List of qubits.

Algorithms

This module provides the circuit implementation for Quantum Fourier Transform.

qft(N=1)[source]

Quantum Fourier Transform operator on N qubits.

Parameters:

N : int

Number of qubits.

Returns:

QFT: qobj

Quantum Fourier transform operator.

qft_steps(N=1, swapping=True)[source]

Quantum Fourier Transform operator on N qubits returning the individual steps as unitary matrices operating from left to right.

Parameters:

N: int

Number of qubits.

swap: boolean

Flag indicating sequence of swap gates to be applied at the end or not.

Returns:

U_step_list: list of qobj

List of Hadamard and controlled rotation gates implementing QFT.

qft_gate_sequence(N=1, swapping=True)[source]

Quantum Fourier Transform operator on N qubits returning the gate sequence.

Parameters:

N: int

Number of qubits.

swap: boolean

Flag indicating sequence of swap gates to be applied at the end or not.

Returns:

qc: instance of QubitCircuit

Gate sequence of Hadamard and controlled rotation gates implementing QFT.

non-Markovian Solvers

This module contains an implementation of the non-Markovian transfer tensor method (TTM), introduced in [1].

[1] Javier Cerrillo and Jianshu Cao, Phys. Rev. Lett 112, 110401 (2014)

ttmsolve(dynmaps, rho0, times, e_ops=[], learningtimes=None, tensors=None, **kwargs)[source]

Solve time-evolution using the Transfer Tensor Method, based on a set of precomputed dynamical maps.

Parameters:

dynmaps : list of qutip.Qobj

List of precomputed dynamical maps (superoperators), or a callback function that returns the superoperator at a given time.

rho0 : qutip.Qobj

Initial density matrix or state vector (ket).

times : array_like

list of times \(t_n\) at which to compute \(\rho(t_n)\). Must be uniformily spaced.

e_ops : list of qutip.Qobj / callback function

single operator or list of operators for which to evaluate expectation values.

learningtimes : array_like

list of times \(t_k\) for which we have knowledge of the dynamical maps \(E(t_k)\).

tensors : array_like

optional list of precomputed tensors \(T_k\)

kwargs : dictionary

Optional keyword arguments. See qutip.nonmarkov.ttm.TTMSolverOptions.

Returns:

output: qutip.solver.Result

An instance of the class qutip.solver.Result.

Optimal control

Wrapper functions that will manage the creation of the objects, build the configuration, and execute the algorithm required to optimise a set of ctrl pulses for a given (quantum) system. The fidelity error is some measure of distance of the system evolution from the given target evolution in the time allowed for the evolution. The functions minimise this fidelity error wrt the piecewise control amplitudes in the timeslots

There are currently two quantum control pulse optmisations algorithms implemented in this library. There are accessible through the methods in this module. Both the algorithms use the scipy.optimize methods to minimise the fidelity error with respect to to variables that define the pulse.

GRAPE

The default algorithm (as it was implemented here first) is GRAPE GRadient Ascent Pulse Engineering [1][2]. It uses a gradient based method such as BFGS to minimise the fidelity error. This makes convergence very quick when an exact gradient can be calculated, but this limits the factors that can taken into account in the fidelity.

CRAB

The CRAB [3][4] algorithm was developed at the University of Ulm. In full it is the Chopped RAndom Basis algorithm. The main difference is that it reduces the number of optimisation variables by defining the control pulses by expansions of basis functions, where the variables are the coefficients. Typically a Fourier series is chosen, i.e. the variables are the Fourier coefficients. Therefore it does not need to compute an explicit gradient. By default it uses the Nelder-Mead method for fidelity error minimisation.

References

  1. N Khaneja et. al. Optimal control of coupled spin dynamics: Design of NMR pulse sequences by gradient ascent algorithms. J. Magn. Reson. 172, 296–305 (2005).
  2. Shai Machnes et.al DYNAMO - Dynamic Framework for Quantum Optimal Control arXiv.1011.4874
  3. Doria, P., Calarco, T. & Montangero, S. Optimal Control Technique for Many-Body Quantum Dynamics. Phys. Rev. Lett. 106, 1–4 (2011).
  4. Caneva, T., Calarco, T. & Montangero, S. Chopped random-basis quantum optimization. Phys. Rev. A - At. Mol. Opt. Phys. 84, (2011).
optimize_pulse(drift, ctrls, initial, target, num_tslots=None, evo_time=None, tau=None, amp_lbound=None, amp_ubound=None, fid_err_targ=1e-10, min_grad=1e-10, max_iter=500, max_wall_time=180, alg='GRAPE', alg_params=None, optim_params=None, optim_method='DEF', method_params=None, optim_alg=None, max_metric_corr=None, accuracy_factor=None, dyn_type='GEN_MAT', dyn_params=None, prop_type='DEF', prop_params=None, fid_type='DEF', fid_params=None, phase_option=None, fid_err_scale_factor=None, tslot_type='DEF', tslot_params=None, amp_update_mode=None, init_pulse_type='DEF', init_pulse_params=None, pulse_scaling=1.0, pulse_offset=0.0, ramping_pulse_type=None, ramping_pulse_params=None, log_level=0, out_file_ext=None, gen_stats=False)[source]

Optimise a control pulse to minimise the fidelity error. The dynamics of the system in any given timeslot are governed by the combined dynamics generator, i.e. the sum of the drift+ctrl_amp[j]*ctrls[j] The control pulse is an [n_ts, n_ctrls)] array of piecewise amplitudes Starting from an intital (typically random) pulse, a multivariable optimisation algorithm attempts to determines the optimal values for the control pulse to minimise the fidelity error The fidelity error is some measure of distance of the system evolution from the given target evolution in the time allowed for the evolution.

Parameters:

drift : Qobj or list of Qobj

the underlying dynamics generator of the system can provide list (of length num_tslots) for time dependent drift

ctrls : List of Qobj or array like [num_tslots, evo_time]

a list of control dynamics generators. These are scaled by the amplitudes to alter the overall dynamics Array like imput can be provided for time dependent control generators

initial : Qobj

starting point for the evolution. Typically the identity matrix

target : Qobj

target transformation, e.g. gate or state, for the time evolution

num_tslots : integer or None

number of timeslots. None implies that timeslots will be given in the tau array

evo_time : float or None

total time for the evolution None implies that timeslots will be given in the tau array

tau : array[num_tslots] of floats or None

durations for the timeslots. if this is given then num_tslots and evo_time are dervived from it None implies that timeslot durations will be equal and calculated as evo_time/num_tslots

amp_lbound : float or list of floats

lower boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

amp_ubound : float or list of floats

upper boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

fid_err_targ : float

Fidelity error target. Pulse optimisation will terminate when the fidelity error falls below this value

mim_grad : float

Minimum gradient. When the sum of the squares of the gradients wrt to the control amplitudes falls below this value, the optimisation terminates, assuming local minima

max_iter : integer

Maximum number of iterations of the optimisation algorithm

max_wall_time : float

Maximum allowed elapsed time for the optimisation algorithm

alg : string

Algorithm to use in pulse optimisation. Options are:

‘GRAPE’ (default) - GRadient Ascent Pulse Engineering ‘CRAB’ - Chopped RAndom Basis

alg_params : Dictionary

options that are specific to the algorithm see above

optim_params : Dictionary

The key value pairs are the attribute name and value used to set attribute values Note: attributes are created if they do not exist already, and are overwritten if they do. Note: method_params are applied afterwards and so may override these

optim_method : string

a scipy.optimize.minimize method that will be used to optimise the pulse for minimum fidelity error Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result in calling these specific scipy.optimize methods Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards capatibility reasons. Supplying DEF will given alg dependent result:

GRAPE - Default optim_method is FMIN_L_BFGS_B CRAB - Default optim_method is FMIN

method_params : dict

Parameters for the optim_method. Note that where there is an attribute of the Optimizer object or the termination_conditions matching the key that attribute. Otherwise, and in some case also, they are assumed to be method_options for the scipy.optimize.minimize method.

optim_alg : string

Deprecated. Use optim_method.

max_metric_corr : integer

Deprecated. Use method_params instead

accuracy_factor : float

Deprecated. Use method_params instead

dyn_type : string

Dynamics type, i.e. the type of matrix used to describe the dynamics. Options are UNIT, GEN_MAT, SYMPL (see Dynamics classes for details)

dyn_params : dict

Parameters for the Dynamics object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

prop_type : string

Propagator type i.e. the method used to calculate the propagtors and propagtor gradient for each timeslot options are DEF, APPROX, DIAG, FRECHET, AUG_MAT DEF will use the default for the specific dyn_type (see PropagatorComputer classes for details)

prop_params : dict

Parameters for the PropagatorComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

fid_type : string

Fidelity error (and fidelity error gradient) computation method Options are DEF, UNIT, TRACEDIFF, TD_APPROX DEF will use the default for the specific dyn_type (See FidelityComputer classes for details)

fid_params : dict

Parameters for the FidelityComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

phase_option : string

Deprecated. Pass in fid_params instead.

fid_err_scale_factor : float

Deprecated. Use scale_factor key in fid_params instead.

tslot_type : string

Method for computing the dynamics generators, propagators and evolution in the timeslots. Options: DEF, UPDATE_ALL, DYNAMIC UPDATE_ALL is the only one that currently works (See TimeslotComputer classes for details)

tslot_params : dict

Parameters for the TimeslotComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

amp_update_mode : string

Deprecated. Use tslot_type instead.

init_pulse_type : string

type / shape of pulse(s) used to initialise the the control amplitudes. Options (GRAPE) include:

RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW

DEF is RND (see PulseGen classes for details) For the CRAB the this the guess_pulse_type.

init_pulse_params : dict

Parameters for the initial / guess pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

pulse_scaling : float

Linear scale factor for generated initial / guess pulses By default initial pulses are generated with amplitudes in the range (-1.0, 1.0). These will be scaled by this parameter

pulse_offset : float

Linear offset for the pulse. That is this value will be added to any initial / guess pulses generated.

ramping_pulse_type : string

Type of pulse used to modulate the control pulse. It’s intended use for a ramping modulation, which is often required in experimental setups. This is only currently implemented in CRAB. GAUSSIAN_EDGE was added for this purpose.

ramping_pulse_params : dict

Parameters for the ramping pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

log_level : integer

level of messaging output from the logger. Options are attributes of qutip.logging_utils, in decreasing levels of messaging, are: DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL Anything WARN or above is effectively ‘quiet’ execution, assuming everything runs as expected. The default NOTSET implies that the level will be taken from the QuTiP settings file, which by default is WARN

out_file_ext : string or None

files containing the initial and final control pulse amplitudes are saved to the current directory. The default name will be postfixed with this extension Setting this to None will suppress the output of files

gen_stats : boolean

if set to True then statistics for the optimisation run will be generated - accessible through attributes of the stats object

Returns:

opt : OptimResult

Returns instance of OptimResult, which has attributes giving the reason for termination, final fidelity error, final evolution final amplitudes, statistics etc

optimize_pulse_unitary(H_d, H_c, U_0, U_targ, num_tslots=None, evo_time=None, tau=None, amp_lbound=None, amp_ubound=None, fid_err_targ=1e-10, min_grad=1e-10, max_iter=500, max_wall_time=180, alg='GRAPE', alg_params=None, optim_params=None, optim_method='DEF', method_params=None, optim_alg=None, max_metric_corr=None, accuracy_factor=None, phase_option='PSU', dyn_params=None, prop_params=None, fid_params=None, tslot_type='DEF', tslot_params=None, amp_update_mode=None, init_pulse_type='DEF', init_pulse_params=None, pulse_scaling=1.0, pulse_offset=0.0, ramping_pulse_type=None, ramping_pulse_params=None, log_level=0, out_file_ext=None, gen_stats=False)[source]

Optimise a control pulse to minimise the fidelity error, assuming that the dynamics of the system are generated by unitary operators. This function is simply a wrapper for optimize_pulse, where the appropriate options for unitary dynamics are chosen and the parameter names are in the format familiar to unitary dynamics The dynamics of the system in any given timeslot are governed by the combined Hamiltonian, i.e. the sum of the H_d + ctrl_amp[j]*H_c[j] The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes Starting from an intital (typically random) pulse, a multivariable optimisation algorithm attempts to determines the optimal values for the control pulse to minimise the fidelity error The maximum fidelity for a unitary system is 1, i.e. when the time evolution resulting from the pulse is equivalent to the target. And therefore the fidelity error is 1 - fidelity

Parameters:

H_d : Qobj or list of Qobj

Drift (aka system) the underlying Hamiltonian of the system can provide list (of length num_tslots) for time dependent drift

H_c : List of Qobj or array like [num_tslots, evo_time]

a list of control Hamiltonians. These are scaled by the amplitudes to alter the overall dynamics Array like imput can be provided for time dependent control generators

U_0 : Qobj

starting point for the evolution. Typically the identity matrix

U_targ : Qobj

target transformation, e.g. gate or state, for the time evolution

num_tslots : integer or None

number of timeslots. None implies that timeslots will be given in the tau array

evo_time : float or None

total time for the evolution None implies that timeslots will be given in the tau array

tau : array[num_tslots] of floats or None

durations for the timeslots. if this is given then num_tslots and evo_time are dervived from it None implies that timeslot durations will be equal and calculated as evo_time/num_tslots

amp_lbound : float or list of floats

lower boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

amp_ubound : float or list of floats

upper boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

fid_err_targ : float

Fidelity error target. Pulse optimisation will terminate when the fidelity error falls below this value

mim_grad : float

Minimum gradient. When the sum of the squares of the gradients wrt to the control amplitudes falls below this value, the optimisation terminates, assuming local minima

max_iter : integer

Maximum number of iterations of the optimisation algorithm

max_wall_time : float

Maximum allowed elapsed time for the optimisation algorithm

alg : string

Algorithm to use in pulse optimisation. Options are:

‘GRAPE’ (default) - GRadient Ascent Pulse Engineering ‘CRAB’ - Chopped RAndom Basis

alg_params : Dictionary

options that are specific to the algorithm see above

optim_params : Dictionary

The key value pairs are the attribute name and value used to set attribute values Note: attributes are created if they do not exist already, and are overwritten if they do. Note: method_params are applied afterwards and so may override these

optim_method : string

a scipy.optimize.minimize method that will be used to optimise the pulse for minimum fidelity error Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result in calling these specific scipy.optimize methods Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards capatibility reasons. Supplying DEF will given alg dependent result:

GRAPE - Default optim_method is FMIN_L_BFGS_B CRAB - Default optim_method is FMIN

method_params : dict

Parameters for the optim_method. Note that where there is an attribute of the Optimizer object or the termination_conditions matching the key that attribute. Otherwise, and in some case also, they are assumed to be method_options for the scipy.optimize.minimize method.

optim_alg : string

Deprecated. Use optim_method.

max_metric_corr : integer

Deprecated. Use method_params instead

accuracy_factor : float

Deprecated. Use method_params instead

phase_option : string

determines how global phase is treated in fidelity calculations (fid_type=’UNIT’ only). Options:

PSU - global phase ignored SU - global phase included

dyn_params : dict

Parameters for the Dynamics object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

prop_params : dict

Parameters for the PropagatorComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

fid_params : dict

Parameters for the FidelityComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

tslot_type : string

Method for computing the dynamics generators, propagators and evolution in the timeslots. Options: DEF, UPDATE_ALL, DYNAMIC UPDATE_ALL is the only one that currently works (See TimeslotComputer classes for details)

tslot_params : dict

Parameters for the TimeslotComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

amp_update_mode : string

Deprecated. Use tslot_type instead.

init_pulse_type : string

type / shape of pulse(s) used to initialise the the control amplitudes. Options (GRAPE) include:

RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW DEF is RND

(see PulseGen classes for details) For the CRAB the this the guess_pulse_type.

init_pulse_params : dict

Parameters for the initial / guess pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

pulse_scaling : float

Linear scale factor for generated initial / guess pulses By default initial pulses are generated with amplitudes in the range (-1.0, 1.0). These will be scaled by this parameter

pulse_offset : float

Linear offset for the pulse. That is this value will be added to any initial / guess pulses generated.

ramping_pulse_type : string

Type of pulse used to modulate the control pulse. It’s intended use for a ramping modulation, which is often required in experimental setups. This is only currently implemented in CRAB. GAUSSIAN_EDGE was added for this purpose.

ramping_pulse_params : dict

Parameters for the ramping pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

log_level : integer

level of messaging output from the logger. Options are attributes of qutip.logging_utils, in decreasing levels of messaging, are: DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL Anything WARN or above is effectively ‘quiet’ execution, assuming everything runs as expected. The default NOTSET implies that the level will be taken from the QuTiP settings file, which by default is WARN

out_file_ext : string or None

files containing the initial and final control pulse amplitudes are saved to the current directory. The default name will be postfixed with this extension Setting this to None will suppress the output of files

gen_stats : boolean

if set to True then statistics for the optimisation run will be generated - accessible through attributes of the stats object

Returns:

opt : OptimResult

Returns instance of OptimResult, which has attributes giving the reason for termination, final fidelity error, final evolution final amplitudes, statistics etc

create_pulse_optimizer(drift, ctrls, initial, target, num_tslots=None, evo_time=None, tau=None, amp_lbound=None, amp_ubound=None, fid_err_targ=1e-10, min_grad=1e-10, max_iter=500, max_wall_time=180, alg='GRAPE', alg_params=None, optim_params=None, optim_method='DEF', method_params=None, optim_alg=None, max_metric_corr=None, accuracy_factor=None, dyn_type='GEN_MAT', dyn_params=None, prop_type='DEF', prop_params=None, fid_type='DEF', fid_params=None, phase_option=None, fid_err_scale_factor=None, tslot_type='DEF', tslot_params=None, amp_update_mode=None, init_pulse_type='DEF', init_pulse_params=None, pulse_scaling=1.0, pulse_offset=0.0, ramping_pulse_type=None, ramping_pulse_params=None, log_level=0, gen_stats=False)[source]

Generate the objects of the appropriate subclasses required for the pulse optmisation based on the parameters given Note this method may be preferable to calling optimize_pulse if more detailed configuration is required before running the optmisation algorthim, or the algorithm will be run many times, for instances when trying to finding global the optimum or minimum time optimisation

Parameters:

drift : Qobj or list of Qobj

the underlying dynamics generator of the system can provide list (of length num_tslots) for time dependent drift

ctrls : List of Qobj or array like [num_tslots, evo_time]

a list of control dynamics generators. These are scaled by the amplitudes to alter the overall dynamics Array like imput can be provided for time dependent control generators

initial : Qobj

starting point for the evolution. Typically the identity matrix

target : Qobj

target transformation, e.g. gate or state, for the time evolution

num_tslots : integer or None

number of timeslots. None implies that timeslots will be given in the tau array

evo_time : float or None

total time for the evolution None implies that timeslots will be given in the tau array

tau : array[num_tslots] of floats or None

durations for the timeslots. if this is given then num_tslots and evo_time are dervived from it None implies that timeslot durations will be equal and calculated as evo_time/num_tslots

amp_lbound : float or list of floats

lower boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

amp_ubound : float or list of floats

upper boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

fid_err_targ : float

Fidelity error target. Pulse optimisation will terminate when the fidelity error falls below this value

mim_grad : float

Minimum gradient. When the sum of the squares of the gradients wrt to the control amplitudes falls below this value, the optimisation terminates, assuming local minima

max_iter : integer

Maximum number of iterations of the optimisation algorithm

max_wall_time : float

Maximum allowed elapsed time for the optimisation algorithm

alg : string

Algorithm to use in pulse optimisation. Options are:

‘GRAPE’ (default) - GRadient Ascent Pulse Engineering ‘CRAB’ - Chopped RAndom Basis

alg_params : Dictionary

options that are specific to the algorithm see above

optim_params : Dictionary

The key value pairs are the attribute name and value used to set attribute values Note: attributes are created if they do not exist already, and are overwritten if they do. Note: method_params are applied afterwards and so may override these

optim_method : string

a scipy.optimize.minimize method that will be used to optimise the pulse for minimum fidelity error Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result in calling these specific scipy.optimize methods Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards capatibility reasons. Supplying DEF will given alg dependent result:

  • GRAPE - Default optim_method is FMIN_L_BFGS_B
  • CRAB - Default optim_method is Nelder-Mead

method_params : dict

Parameters for the optim_method. Note that where there is an attribute of the Optimizer object or the termination_conditions matching the key that attribute. Otherwise, and in some case also, they are assumed to be method_options for the scipy.optimize.minimize method.

optim_alg : string

Deprecated. Use optim_method.

max_metric_corr : integer

Deprecated. Use method_params instead

accuracy_factor : float

Deprecated. Use method_params instead

dyn_type : string

Dynamics type, i.e. the type of matrix used to describe the dynamics. Options are UNIT, GEN_MAT, SYMPL (see Dynamics classes for details)

dyn_params : dict

Parameters for the Dynamics object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

prop_type : string

Propagator type i.e. the method used to calculate the propagtors and propagtor gradient for each timeslot options are DEF, APPROX, DIAG, FRECHET, AUG_MAT DEF will use the default for the specific dyn_type (see PropagatorComputer classes for details)

prop_params : dict

Parameters for the PropagatorComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

fid_type : string

Fidelity error (and fidelity error gradient) computation method Options are DEF, UNIT, TRACEDIFF, TD_APPROX DEF will use the default for the specific dyn_type (See FidelityComputer classes for details)

fid_params : dict

Parameters for the FidelityComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

phase_option : string

Deprecated. Pass in fid_params instead.

fid_err_scale_factor : float

Deprecated. Use scale_factor key in fid_params instead.

tslot_type : string

Method for computing the dynamics generators, propagators and evolution in the timeslots. Options: DEF, UPDATE_ALL, DYNAMIC UPDATE_ALL is the only one that currently works (See TimeslotComputer classes for details)

tslot_params : dict

Parameters for the TimeslotComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

amp_update_mode : string

Deprecated. Use tslot_type instead.

init_pulse_type : string

type / shape of pulse(s) used to initialise the the control amplitudes. Options (GRAPE) include:

RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW DEF is RND

(see PulseGen classes for details) For the CRAB the this the guess_pulse_type.

init_pulse_params : dict

Parameters for the initial / guess pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

pulse_scaling : float

Linear scale factor for generated initial / guess pulses By default initial pulses are generated with amplitudes in the range (-1.0, 1.0). These will be scaled by this parameter

pulse_offset : float

Linear offset for the pulse. That is this value will be added to any initial / guess pulses generated.

ramping_pulse_type : string

Type of pulse used to modulate the control pulse. It’s intended use for a ramping modulation, which is often required in experimental setups. This is only currently implemented in CRAB. GAUSSIAN_EDGE was added for this purpose.

ramping_pulse_params : dict

Parameters for the ramping pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

log_level : integer

level of messaging output from the logger. Options are attributes of qutip.logging_utils, in decreasing levels of messaging, are: DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL Anything WARN or above is effectively ‘quiet’ execution, assuming everything runs as expected. The default NOTSET implies that the level will be taken from the QuTiP settings file, which by default is WARN

gen_stats : boolean

if set to True then statistics for the optimisation run will be generated - accessible through attributes of the stats object

Returns:

opt : Optimizer

Instance of an Optimizer, through which the Config, Dynamics, PulseGen, and TerminationConditions objects can be accessed as attributes. The PropagatorComputer, FidelityComputer and TimeslotComputer objects can be accessed as attributes of the Dynamics object, e.g. optimizer.dynamics.fid_computer The optimisation can be run through the optimizer.run_optimization

opt_pulse_crab(drift, ctrls, initial, target, num_tslots=None, evo_time=None, tau=None, amp_lbound=None, amp_ubound=None, fid_err_targ=1e-05, max_iter=500, max_wall_time=180, alg_params=None, num_coeffs=None, init_coeff_scaling=1.0, optim_params=None, optim_method='fmin', method_params=None, dyn_type='GEN_MAT', dyn_params=None, prop_type='DEF', prop_params=None, fid_type='DEF', fid_params=None, tslot_type='DEF', tslot_params=None, guess_pulse_type=None, guess_pulse_params=None, guess_pulse_scaling=1.0, guess_pulse_offset=0.0, guess_pulse_action='MODULATE', ramping_pulse_type=None, ramping_pulse_params=None, log_level=0, out_file_ext=None, gen_stats=False)[source]

Optimise a control pulse to minimise the fidelity error. The dynamics of the system in any given timeslot are governed by the combined dynamics generator, i.e. the sum of the drift+ctrl_amp[j]*ctrls[j] The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes. The CRAB algorithm uses basis function coefficents as the variables to optimise. It does NOT use any gradient function. A multivariable optimisation algorithm attempts to determines the optimal values for the control pulse to minimise the fidelity error The fidelity error is some measure of distance of the system evolution from the given target evolution in the time allowed for the evolution.

Parameters:

drift : Qobj or list of Qobj

the underlying dynamics generator of the system can provide list (of length num_tslots) for time dependent drift

ctrls : List of Qobj or array like [num_tslots, evo_time]

a list of control dynamics generators. These are scaled by the amplitudes to alter the overall dynamics Array like imput can be provided for time dependent control generators

initial : Qobj

starting point for the evolution. Typically the identity matrix

target : Qobj

target transformation, e.g. gate or state, for the time evolution

num_tslots : integer or None

number of timeslots. None implies that timeslots will be given in the tau array

evo_time : float or None

total time for the evolution None implies that timeslots will be given in the tau array

tau : array[num_tslots] of floats or None

durations for the timeslots. if this is given then num_tslots and evo_time are dervived from it None implies that timeslot durations will be equal and calculated as evo_time/num_tslots

amp_lbound : float or list of floats

lower boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

amp_ubound : float or list of floats

upper boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

fid_err_targ : float

Fidelity error target. Pulse optimisation will terminate when the fidelity error falls below this value

max_iter : integer

Maximum number of iterations of the optimisation algorithm

max_wall_time : float

Maximum allowed elapsed time for the optimisation algorithm

alg_params : Dictionary

options that are specific to the algorithm see above

optim_params : Dictionary

The key value pairs are the attribute name and value used to set attribute values Note: attributes are created if they do not exist already, and are overwritten if they do. Note: method_params are applied afterwards and so may override these

coeff_scaling : float

Linear scale factor for the random basis coefficients By default these range from -1.0 to 1.0 Note this is overridden by alg_params (if given there)

num_coeffs : integer

Number of coefficients used for each basis function Note this is calculated automatically based on the dimension of the dynamics if not given. It is crucial to the performane of the algorithm that it is set as low as possible, while still giving high enough frequencies. Note this is overridden by alg_params (if given there)

optim_method : string

Multi-variable optimisation method The only tested options are ‘fmin’ and ‘Nelder-mead’ In theory any non-gradient method implemented in scipy.optimize.mininize could be used.

method_params : dict

Parameters for the optim_method. Note that where there is an attribute of the Optimizer object or the termination_conditions matching the key that attribute. Otherwise, and in some case also, they are assumed to be method_options for the scipy.optimize.minimize method. The commonly used parameter are:

xtol - limit on variable change for convergence ftol - limit on fidelity error change for convergence

dyn_type : string

Dynamics type, i.e. the type of matrix used to describe the dynamics. Options are UNIT, GEN_MAT, SYMPL (see Dynamics classes for details)

dyn_params : dict

Parameters for the Dynamics object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

prop_type : string

Propagator type i.e. the method used to calculate the propagtors and propagtor gradient for each timeslot options are DEF, APPROX, DIAG, FRECHET, AUG_MAT DEF will use the default for the specific dyn_type (see PropagatorComputer classes for details)

prop_params : dict

Parameters for the PropagatorComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

fid_type : string

Fidelity error (and fidelity error gradient) computation method Options are DEF, UNIT, TRACEDIFF, TD_APPROX DEF will use the default for the specific dyn_type (See FidelityComputer classes for details)

fid_params : dict

Parameters for the FidelityComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

tslot_type : string

Method for computing the dynamics generators, propagators and evolution in the timeslots. Options: DEF, UPDATE_ALL, DYNAMIC UPDATE_ALL is the only one that currently works (See TimeslotComputer classes for details)

tslot_params : dict

Parameters for the TimeslotComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

guess_pulse_type : string

type / shape of pulse(s) used modulate the control amplitudes. Options include:

RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW, GAUSSIAN

Default is None

guess_pulse_params : dict

Parameters for the guess pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

guess_pulse_action : string

Determines how the guess pulse is applied to the pulse generated by the basis expansion. Options are: MODULATE, ADD Default is MODULATE

pulse_scaling : float

Linear scale factor for generated guess pulses By default initial pulses are generated with amplitudes in the range (-1.0, 1.0). These will be scaled by this parameter

pulse_offset : float

Linear offset for the pulse. That is this value will be added to any guess pulses generated.

ramping_pulse_type : string

Type of pulse used to modulate the control pulse. It’s intended use for a ramping modulation, which is often required in experimental setups. This is only currently implemented in CRAB. GAUSSIAN_EDGE was added for this purpose.

ramping_pulse_params : dict

Parameters for the ramping pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

log_level : integer

level of messaging output from the logger. Options are attributes of qutip.logging_utils, in decreasing levels of messaging, are: DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL Anything WARN or above is effectively ‘quiet’ execution, assuming everything runs as expected. The default NOTSET implies that the level will be taken from the QuTiP settings file, which by default is WARN

out_file_ext : string or None

files containing the initial and final control pulse amplitudes are saved to the current directory. The default name will be postfixed with this extension Setting this to None will suppress the output of files

gen_stats : boolean

if set to True then statistics for the optimisation run will be generated - accessible through attributes of the stats object

Returns:

opt : OptimResult

Returns instance of OptimResult, which has attributes giving the reason for termination, final fidelity error, final evolution final amplitudes, statistics etc

opt_pulse_crab_unitary(H_d, H_c, U_0, U_targ, num_tslots=None, evo_time=None, tau=None, amp_lbound=None, amp_ubound=None, fid_err_targ=1e-05, max_iter=500, max_wall_time=180, alg_params=None, num_coeffs=None, init_coeff_scaling=1.0, optim_params=None, optim_method='fmin', method_params=None, phase_option='PSU', dyn_params=None, prop_params=None, fid_params=None, tslot_type='DEF', tslot_params=None, guess_pulse_type=None, guess_pulse_params=None, guess_pulse_scaling=1.0, guess_pulse_offset=0.0, guess_pulse_action='MODULATE', ramping_pulse_type=None, ramping_pulse_params=None, log_level=0, out_file_ext=None, gen_stats=False)[source]

Optimise a control pulse to minimise the fidelity error, assuming that the dynamics of the system are generated by unitary operators. This function is simply a wrapper for optimize_pulse, where the appropriate options for unitary dynamics are chosen and the parameter names are in the format familiar to unitary dynamics The dynamics of the system in any given timeslot are governed by the combined Hamiltonian, i.e. the sum of the H_d + ctrl_amp[j]*H_c[j] The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes

The CRAB algorithm uses basis function coefficents as the variables to optimise. It does NOT use any gradient function. A multivariable optimisation algorithm attempts to determines the optimal values for the control pulse to minimise the fidelity error The fidelity error is some measure of distance of the system evolution from the given target evolution in the time allowed for the evolution.

Parameters:

H_d : Qobj or list of Qobj

Drift (aka system) the underlying Hamiltonian of the system can provide list (of length num_tslots) for time dependent drift

H_c : List of Qobj or array like [num_tslots, evo_time]

a list of control Hamiltonians. These are scaled by the amplitudes to alter the overall dynamics Array like imput can be provided for time dependent control generators

U_0 : Qobj

starting point for the evolution. Typically the identity matrix

U_targ : Qobj

target transformation, e.g. gate or state, for the time evolution

num_tslots : integer or None

number of timeslots. None implies that timeslots will be given in the tau array

evo_time : float or None

total time for the evolution None implies that timeslots will be given in the tau array

tau : array[num_tslots] of floats or None

durations for the timeslots. if this is given then num_tslots and evo_time are dervived from it None implies that timeslot durations will be equal and calculated as evo_time/num_tslots

amp_lbound : float or list of floats

lower boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

amp_ubound : float or list of floats

upper boundaries for the control amplitudes Can be a scalar value applied to all controls or a list of bounds for each control

fid_err_targ : float

Fidelity error target. Pulse optimisation will terminate when the fidelity error falls below this value

max_iter : integer

Maximum number of iterations of the optimisation algorithm

max_wall_time : float

Maximum allowed elapsed time for the optimisation algorithm

alg_params : Dictionary

options that are specific to the algorithm see above

optim_params : Dictionary

The key value pairs are the attribute name and value used to set attribute values Note: attributes are created if they do not exist already, and are overwritten if they do. Note: method_params are applied afterwards and so may override these

coeff_scaling : float

Linear scale factor for the random basis coefficients By default these range from -1.0 to 1.0 Note this is overridden by alg_params (if given there)

num_coeffs : integer

Number of coefficients used for each basis function Note this is calculated automatically based on the dimension of the dynamics if not given. It is crucial to the performane of the algorithm that it is set as low as possible, while still giving high enough frequencies. Note this is overridden by alg_params (if given there)

optim_method : string

Multi-variable optimisation method The only tested options are ‘fmin’ and ‘Nelder-mead’ In theory any non-gradient method implemented in scipy.optimize.mininize could be used.

method_params : dict

Parameters for the optim_method. Note that where there is an attribute of the Optimizer object or the termination_conditions matching the key that attribute. Otherwise, and in some case also, they are assumed to be method_options for the scipy.optimize.minimize method. The commonly used parameter are:

xtol - limit on variable change for convergence ftol - limit on fidelity error change for convergence

phase_option : string

determines how global phase is treated in fidelity calculations (fid_type=’UNIT’ only). Options:

PSU - global phase ignored SU - global phase included

dyn_params : dict

Parameters for the Dynamics object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

prop_params : dict

Parameters for the PropagatorComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

fid_params : dict

Parameters for the FidelityComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

tslot_type : string

Method for computing the dynamics generators, propagators and evolution in the timeslots. Options: DEF, UPDATE_ALL, DYNAMIC UPDATE_ALL is the only one that currently works (See TimeslotComputer classes for details)

tslot_params : dict

Parameters for the TimeslotComputer object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

guess_pulse_type : string

type / shape of pulse(s) used modulate the control amplitudes. Options include:

RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW, GAUSSIAN

Default is None

guess_pulse_params : dict

Parameters for the guess pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

guess_pulse_action : string

Determines how the guess pulse is applied to the pulse generated by the basis expansion. Options are: MODULATE, ADD Default is MODULATE

pulse_scaling : float

Linear scale factor for generated guess pulses By default initial pulses are generated with amplitudes in the range (-1.0, 1.0). These will be scaled by this parameter

pulse_offset : float

Linear offset for the pulse. That is this value will be added to any guess pulses generated.

ramping_pulse_type : string

Type of pulse used to modulate the control pulse. It’s intended use for a ramping modulation, which is often required in experimental setups. This is only currently implemented in CRAB. GAUSSIAN_EDGE was added for this purpose.

ramping_pulse_params : dict

Parameters for the ramping pulse generator object The key value pairs are assumed to be attribute name value pairs They applied after the object is created

log_level : integer

level of messaging output from the logger. Options are attributes of qutip.logging_utils, in decreasing levels of messaging, are: DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL Anything WARN or above is effectively ‘quiet’ execution, assuming everything runs as expected. The default NOTSET implies that the level will be taken from the QuTiP settings file, which by default is WARN

out_file_ext : string or None

files containing the initial and final control pulse amplitudes are saved to the current directory. The default name will be postfixed with this extension Setting this to None will suppress the output of files

gen_stats : boolean

if set to True then statistics for the optimisation run will be generated - accessible through attributes of the stats object

Returns:

opt : OptimResult

Returns instance of OptimResult, which has attributes giving the reason for termination, final fidelity error, final evolution final amplitudes, statistics etc

Pulse generator - Generate pulses for the timeslots Each class defines a gen_pulse function that produces a float array of size num_tslots. Each class produces a differ type of pulse. See the class and gen_pulse function descriptions for details

create_pulse_gen(pulse_type='RND', dyn=None, pulse_params=None)[source]

Create and return a pulse generator object matching the given type. The pulse generators each produce a different type of pulse, see the gen_pulse function description for details. These are the random pulse options:

RND - Independent random value in each timeslot RNDFOURIER - Fourier series with random coefficients RNDWAVES - Summation of random waves RNDWALK1 - Random change in amplitude each timeslot RNDWALK2 - Random change in amp gradient each timeslot

These are the other non-periodic options:

LIN - Linear, i.e. contant gradient over the time ZERO - special case of the LIN pulse, where the gradient is 0

These are the periodic options

SINE - Sine wave SQUARE - Square wave SAW - Saw tooth wave TRIANGLE - Triangular wave

If a Dynamics object is passed in then this is used in instantiate the PulseGen, meaning that some timeslot and amplitude properties are copied over.

Utilitiy Functions

Graph Theory Routines

This module contains a collection of graph theory routines used mainly to reorder matrices for iterative steady state solvers.

Breadth-First-Search (BFS) of a graph in CSR or CSC matrix format starting from a given node (row). Takes Qobjs and CSR or CSC matrices as inputs.

This function requires a matrix with symmetric structure. Use A+trans(A) if original matrix is not symmetric or not sure.

Parameters:

A : csc_matrix, csr_matrix

Input graph in CSC or CSR matrix format

start : int

Staring node for BFS traversal.

Returns:

order : array

Order in which nodes are traversed from starting node.

levels : array

Level of the nodes in the order that they are traversed.

graph_degree(A)[source]

Returns the degree for the nodes (rows) of a symmetric graph in sparse CSR or CSC format, or a qobj.

Parameters:

A : qobj, csr_matrix, csc_matrix

Input quantum object or csr_matrix.

Returns:

degree : array

Array of integers giving the degree for each node (row).

reverse_cuthill_mckee(A, sym=False)[source]

Returns the permutation array that orders a sparse CSR or CSC matrix in Reverse-Cuthill McKee ordering. Since the input matrix must be symmetric, this routine works on the matrix A+Trans(A) if the sym flag is set to False (Default).

It is assumed by default (sym=False) that the input matrix is not symmetric. This is because it is faster to do A+Trans(A) than it is to check for symmetry for a generic matrix. If you are guaranteed that the matrix is symmetric in structure (values of matrix element do not matter) then set sym=True

Parameters:

A : csc_matrix, csr_matrix

Input sparse CSC or CSR sparse matrix format.

sym : bool {False, True}

Flag to set whether input matrix is symmetric.

Returns:

perm : array

Array of permuted row and column indices.

Notes

This routine is used primarily for internal reordering of Lindblad superoperators for use in iterative solver routines.

References

E. Cuthill and J. McKee, “Reducing the Bandwidth of Sparse Symmetric Matrices”, ACM ‘69 Proceedings of the 1969 24th national conference, (1969).

maximum_bipartite_matching(A, perm_type='row')[source]

Returns an array of row or column permutations that removes nonzero elements from the diagonal of a nonsingular square CSC sparse matrix. Such a permutation is always possible provided that the matrix is nonsingular. This function looks at the structure of the matrix only.

The input matrix will be converted to CSC matrix format if necessary.

Parameters:

A : sparse matrix

Input matrix

perm_type : str {‘row’, ‘column’}

Type of permutation to generate.

Returns:

perm : array

Array of row or column permutations.

Notes

This function relies on a maximum cardinality bipartite matching algorithm based on a breadth-first search (BFS) of the underlying graph[1]_.

References

I. S. Duff, K. Kaya, and B. Ucar, “Design, Implementation, and Analysis of Maximum Transversal Algorithms”, ACM Trans. Math. Softw. 38, no. 2, (2011).

weighted_bipartite_matching(A, perm_type='row')[source]

Returns an array of row permutations that attempts to maximize the product of the ABS values of the diagonal elements in a nonsingular square CSC sparse matrix. Such a permutation is always possible provided that the matrix is nonsingular.

This function looks at both the structure and ABS values of the underlying matrix.

Parameters:

A : csc_matrix

Input matrix

perm_type : str {‘row’, ‘column’}

Type of permutation to generate.

Returns:

perm : array

Array of row or column permutations.

Notes

This function uses a weighted maximum cardinality bipartite matching algorithm based on breadth-first search (BFS). The columns are weighted according to the element of max ABS value in the associated rows and are traversed in descending order by weight. When performing the BFS traversal, the row associated to a given column is the one with maximum weight. Unlike other techniques[1]_, this algorithm does not guarantee the product of the diagonal is maximized. However, this limitation is offset by the substantially faster runtime of this method.

References

I. S. Duff and J. Koster, “The design and use of algorithms for permuting large entries to the diagonal of sparse matrices”, SIAM J. Matrix Anal. and Applics. 20, no. 4, 889 (1997).

Utility Functions

This module contains utility functions that are commonly needed in other qutip modules.

n_thermal(w, w_th)[source]

Return the number of photons in thermal equilibrium for an harmonic oscillator mode with frequency ‘w’, at the temperature described by ‘w_th’ where \(\omega_{\rm th} = k_BT/\hbar\).

Parameters:

w : float or array

Frequency of the oscillator.

w_th : float

The temperature in units of frequency (or the same units as w).

Returns:

n_avg : float or array

Return the number of average photons in thermal equilibrium for a an oscillator with the given frequency and temperature.

linspace_with(start, stop, num=50, elems=[])[source]

Return an array of numbers sampled over specified interval with additional elements added.

Returns num spaced array with elements from elems inserted if not already included in set.

Returned sample array is not evenly spaced if addtional elements are added.

Parameters:

start : int

The starting value of the sequence.

stop : int

The stoping values of the sequence.

num : int, optional

Number of samples to generate.

elems : list/ndarray, optional

Requested elements to include in array

Returns:

samples : ndadrray

Original equally spaced sample array with additional elements added.

clebsch(j1, j2, j3, m1, m2, m3)[source]

Calculates the Clebsch-Gordon coefficient for coupling (j1,m1) and (j2,m2) to give (j3,m3).

Parameters:

j1 : float

Total angular momentum 1.

j2 : float

Total angular momentum 2.

j3 : float

Total angular momentum 3.

m1 : float

z-component of angular momentum 1.

m2 : float

z-component of angular momentum 2.

m3 : float

z-component of angular momentum 3.

Returns:

cg_coeff : float

Requested Clebsch-Gordan coefficient.

convert_unit(value, orig='meV', to='GHz')[source]

Convert an energy from unit orig to unit to.

Parameters:

value : float / array

The energy in the old unit.

orig : string

The name of the original unit (“J”, “eV”, “meV”, “GHz”, “mK”)

to : string

The name of the new unit (“J”, “eV”, “meV”, “GHz”, “mK”)

Returns:

value_new_unit : float / array

The energy in the new unit.

File I/O Functions

file_data_read(filename, sep=None)[source]

Retrieves an array of data from the requested file.

Parameters:

filename : str

Name of file containing reqested data.

sep : str

Seperator used to store data.

Returns:

data : array_like

Data from selected file.

file_data_store(filename, data, numtype='complex', numformat='decimal', sep=', ')[source]

Stores a matrix of data to a file to be read by an external program.

Parameters:

filename : str

Name of data file to be stored, including extension.

data: array_like

Data to be written to file.

numtype : str {‘complex, ‘real’}

Type of numerical data.

numformat : str {‘decimal’,’exp’}

Format for written data.

sep : str

Single-character field seperator. Usually a tab, space, comma, or semicolon.

qload(name)[source]

Loads data file from file named ‘filename.qu’ in current directory.

Parameters:

name : str

Name of data file to be loaded.

Returns:

qobject : instance / array_like

Object retrieved from requested file.

qsave(data, name='qutip_data')[source]

Saves given data to file named ‘filename.qu’ in current directory.

Parameters:

data : instance/array_like

Input Python object to be stored.

filename : str

Name of output data file.

Parallelization

This function provides functions for parallel execution of loops and function mappings, using the builtin Python module multiprocessing.

parfor(func, *args, **kwargs)[source]

Executes a multi-variable function in parallel on the local machine.

Parallel execution of a for-loop over function func for multiple input arguments and keyword arguments.

Note

From QuTiP 3.1, we recommend to use qutip.parallel_map instead of this function.

Parameters:

func : function_type

A function to run in parallel on the local machine. The function ‘func’ accepts a series of arguments that are passed to the function as variables. In general, the function can have multiple input variables, and these arguments must be passed in the same order as they are defined in the function definition. In addition, the user can pass multiple keyword arguments to the function.

The following keyword argument is reserved:

num_cpus : int

Number of CPU’s to use. Default uses maximum number of CPU’s. Performance degrades if num_cpus is larger than the physical CPU count of your machine.

Returns:

result : list

A list with length equal to number of input parameters containing the output from func.

parallel_map(task, values, task_args=(), task_kwargs={}, **kwargs)[source]

Parallel execution of a mapping of values to the function task. This is functionally equivalent to:

result = [task(value, *task_args, **task_kwargs) for value in values]
Parameters:

task : a Python function

The function that is to be called for each value in task_vec.

values : array / list

The list or array of values for which the task function is to be evaluated.

task_args : list / dictionary

The optional additional argument to the task function.

task_kwargs : list / dictionary

The optional additional keyword argument to the task function.

progress_bar : ProgressBar

Progress bar class instance for showing progress.

Returns:

result : list

The result list contains the value of task(value, *task_args, **task_kwargs) for each value in values.

serial_map(task, values, task_args=(), task_kwargs={}, **kwargs)[source]

Serial mapping function with the same call signature as parallel_map, for easy switching between serial and parallel execution. This is functionally equivalent to:

result = [task(value, *task_args, **task_kwargs) for value in values]

This function work as a drop-in replacement of qutip.parallel_map.

Parameters:

task : a Python function

The function that is to be called for each value in task_vec.

values : array / list

The list or array of values for which the task function is to be evaluated.

task_args : list / dictionary

The optional additional argument to the task function.

task_kwargs : list / dictionary

The optional additional keyword argument to the task function.

progress_bar : ProgressBar

Progress bar class instance for showing progress.

Returns:

result : list

The result list contains the value of task(value, *task_args, **task_kwargs) for each value in values.

IPython Notebook Tools

This module contains utility functions for using QuTiP with IPython notebooks.

parfor(task, task_vec, args=None, client=None, view=None, show_scheduling=False, show_progressbar=False)[source]

Call the function tast for each value in task_vec using a cluster of IPython engines. The function task should have the signature task(value, args) or task(value) if args=None.

The client and view are the IPython.parallel client and load-balanced view that will be used in the parfor execution. If these are None, new instances will be created.

Parameters:

task: a Python function

The function that is to be called for each value in task_vec.

task_vec: array / list

The list or array of values for which the task function is to be evaluated.

args: list / dictionary

The optional additional argument to the task function. For example a dictionary with parameter values.

client: IPython.parallel.Client

The IPython.parallel Client instance that will be used in the parfor execution.

view: a IPython.parallel.Client view

The view that is to be used in scheduling the tasks on the IPython cluster. Preferably a load-balanced view, which is obtained from the IPython.parallel.Client instance client by calling, view = client.load_balanced_view().

show_scheduling: bool {False, True}, default False

Display a graph showing how the tasks (the evaluation of task for for the value in task_vec1) was scheduled on the IPython engine cluster.

show_progressbar: bool {False, True}, default False

Display a HTML-based progress bar duing the execution of the parfor loop.

Returns:

result : list

The result list contains the value of task(value, args) for each value in task_vec, that is, it should be equivalent to [task(v, args) for v in task_vec].

parallel_map(task, values, task_args=None, task_kwargs=None, client=None, view=None, progress_bar=None, show_scheduling=False, **kwargs)[source]

Call the function task for each value in values using a cluster of IPython engines. The function task should have the signature task(value, *args, **kwargs).

The client and view are the IPython.parallel client and load-balanced view that will be used in the parfor execution. If these are None, new instances will be created.

Parameters:

task: a Python function

The function that is to be called for each value in task_vec.

values: array / list

The list or array of values for which the task function is to be evaluated.

task_args: list / dictionary

The optional additional argument to the task function.

task_kwargs: list / dictionary

The optional additional keyword argument to the task function.

client: IPython.parallel.Client

The IPython.parallel Client instance that will be used in the parfor execution.

view: a IPython.parallel.Client view

The view that is to be used in scheduling the tasks on the IPython cluster. Preferably a load-balanced view, which is obtained from the IPython.parallel.Client instance client by calling, view = client.load_balanced_view().

show_scheduling: bool {False, True}, default False

Display a graph showing how the tasks (the evaluation of task for for the value in task_vec1) was scheduled on the IPython engine cluster.

show_progressbar: bool {False, True}, default False

Display a HTML-based progress bar during the execution of the parfor loop.

Returns:

result : list

The result list contains the value of task(value, task_args, task_kwargs) for each value in values.

version_table(verbose=False)[source]

Print an HTML-formatted table with version numbers for QuTiP and its dependencies. Use it in a IPython notebook to show which versions of different packages that were used to run the notebook. This should make it possible to reproduce the environment and the calculation later on.

Returns:

version_table: string

Return an HTML-formatted string containing version information for QuTiP dependencies.

Miscellaneous

about()[source]

About box for QuTiP. Gives version numbers for QuTiP, NumPy, SciPy, Cython, and MatPlotLib.

simdiag(ops, evals=True)[source]

Simulateous diagonalization of communting Hermitian matrices..

Parameters:

ops : list/array

list or array of qobjs representing commuting Hermitian operators.

Returns:

eigs : tuple

Tuple of arrays representing eigvecs and eigvals of quantum objects corresponding to simultaneous eigenvectors and eigenvalues for each operator.