Source code for qutip.qip.device.modelprocessor

from import Iterable
import numbers

import numpy as np

from qutip.qobj import Qobj
from qutip.qobjevo import QobjEvo
from qutip.qip.operations.gates import globalphase
from qutip.tensor import tensor
from qutip.mesolve import mesolve
from qutip.qip.circuit import QubitCircuit
from qutip.qip.device.processor import Processor

__all__ = ['ModelProcessor']

[docs]class ModelProcessor(Processor): """ The base class for a circuit processor simulating a physical device, e.g cavityQED, spinchain. The available Hamiltonian of the system is predefined. The processor can simulate the evolution under the given control pulses either numerically or analytically. It cannot be used alone, please refer to the sub-classes. (Only additional attributes are documented here, for others please refer to the parent class :class:`.Processor`) Parameters ---------- N: int The number of component systems. correct_global_phase: boolean, optional If true, the analytical solution will track the global phase. It has no effect on the numerical solution. t1: list or float Characterize the decoherence of amplitude damping for each qubit. A list of size `N` or a float for all qubits. t2: list of float Characterize the decoherence of dephasing for each qubit. A list of size `N` or a float for all qubits. Attributes ---------- params: dict A Python dictionary contains the name and the value of the parameters in the physical realization, such as laser frequency, detuning etc. correct_global_phase: float Save the global phase, the analytical solution will track the global phase. It has no effect on the numerical solution. """ def __init__(self, N, correct_global_phase=True, t1=None, t2=None): super(ModelProcessor, self).__init__(N, t1=t1, t2=t2) self.correct_global_phase = correct_global_phase self.global_phase = 0. self._params = {}
[docs] def to_array(self, params, N): """ Transfer a parameter to an array. """ if isinstance(params, numbers.Real): return np.asarray([params] * N) elif isinstance(params, Iterable): return np.asarray(params)
[docs] def set_up_params(self): """ Save the parameters in the attribute `params` and check the validity. (Defined in subclasses) Notes ----- All parameters will be multiplied by 2*pi for simplicity """ raise NotImplementedError("Parameters should be defined in subclass.")
@property def params(self): return self._params @params.setter def params(self, par): self._params = par
[docs] def run_state(self, init_state=None, analytical=False, qc=None, states=None, **kwargs): """ If `analytical` is False, use :func:`qutip.mesolve` to calculate the time of the state evolution and return the result. Other arguments of mesolve can be given as keyword arguments. If `analytical` is True, calculate the propagator with matrix exponentiation and return a list of matrices. Parameters ---------- init_state: Qobj Initial density matrix or state vector (ket). analytical: boolean If True, calculate the evolution with matrices exponentiation. qc: :class:`.QubitCircuit`, optional A quantum circuit. If given, it first calls the ``load_circuit`` and then calculate the evolution. states: :class:`qutip.Qobj`, optional Old API, same as init_state. **kwargs Keyword arguments for the qutip solver. Returns ------- evo_result: :class:`qutip.solver.Result` If ``analytical`` is False, an instance of the class :class:`qutip.solver.Result` will be returned. If ``analytical`` is True, a list of matrices representation is returned. """ if qc is not None: self.load_circuit(qc) return super(ModelProcessor, self).run_state( init_state=init_state, analytical=analytical, states=states, **kwargs)
[docs] def get_ops_and_u(self): """ Get the labels for each Hamiltonian. Returns ------- ctrls: list The list of Hamiltonians coeffs: array_like The transposed pulse matrix """ return (self.ctrls, self.get_full_coeffs().T)
[docs] def pulse_matrix(self, dt=0.01): """ Generates the pulse matrix for the desired physical system. Returns ------- t, u, labels: Returns the total time and label for every operation. """ ctrls = self.ctrls coeffs = self.get_full_coeffs().T # FIXME This might becomes a problem if new tlist other than # int the default pulses are added. tlist = self.get_full_tlist() dt_list = tlist[1:] - tlist[:-1] t_tot = tlist[-1] num_step = int(np.ceil(t_tot / dt)) t = np.linspace(0, t_tot, num_step) u = np.zeros((len(ctrls), num_step)) t_start = 0 for n in range(len(dt_list)): t_idx_len = int(np.floor(dt_list[n] / dt)) mm = 0 for m in range(len(ctrls)): u[mm, t_start:(t_start + t_idx_len)] = (np.ones(t_idx_len) * coeffs[n, m]) mm += 1 t_start += t_idx_len return t, u, self.get_operators_labels()