Source code for qutip.qip.device.optpulseprocessor

from import Iterable
import warnings
import numbers

import numpy as np

from qutip.qobj import Qobj
import qutip.control.pulseoptim as cpo
from qutip.operators import identity
from qutip.tensor import tensor
from qutip.mesolve import mesolve
from qutip.qip.circuit import QubitCircuit
from qutip.qip.device.processor import Processor
from qutip.qip.operations.gates import gate_sequence_product

__all__ = ['OptPulseProcessor']

[docs]class OptPulseProcessor(Processor): """ A processor, which takes the Hamiltonian available as dynamic generators, calls the `qutip.control.optimize_pulse_unitary` function to find an optimized pulse sequence for the desired quantum circuit. The processor can simulate the evolution under the given control pulses using :func:`qutip.mesolve`. (For attributes documentation, please refer to the parent class :class:`.Processor`) Parameters ---------- N: int The number of component systems. drift: `:class:`qutip.Qobj` The drift Hamiltonian. The size must match the whole quantum system. 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. dims: list The dimension of each component system. Default value is a qubit system of ``dim=[2,2,2,...,2]`` """ def __init__(self, N, drift=None, t1=None, t2=None, dims=None): super(OptPulseProcessor, self).__init__( N, t1=t1, t2=t2, dims=dims) if drift is not None: self.add_drift(drift, list(range(N))) self.spline_kind = "step_func"
[docs] def load_circuit(self, qc, min_fid_err=np.inf, merge_gates=True, setting_args=None, verbose=False, **kwargs): """ Find the pulses realizing a given :class:`.QubitCircuit` using `qutip.control.optimize_pulse_unitary`. Further parameter for for `qutip.control.optimize_pulse_unitary` needs to be given as keyword arguments. By default, it first merge all the gates into one unitary and then find the control pulses for it. It can be turned off and one can set different parameters for different gates. See examples for details. Examples -------- Same parameter for all the gates: .. code-block:: python qc = QubitCircuit(N=1) qc.add_gate("SNOT", 0) num_tslots = 10 evo_time = 10 processor = OptPulseProcessor(N=1, drift=sigmaz(), ctrls=[sigmax()]) # num_tslots and evo_time are two keyword arguments tlist, coeffs = processor.load_circuit( qc, num_tslots=num_tslots, evo_time=evo_time) Different parameters for different gates: .. code-block:: python qc = QubitCircuit(N=2) qc.add_gate("SNOT", 0) qc.add_gate("SWAP", targets=[0, 1]) qc.add_gate('CNOT', controls=1, targets=[0]) processor = OptPulseProcessor(N=2, drift=tensor([sigmaz()]*2)) processor.add_control(sigmax(), cyclic_permutation=True) processor.add_control(sigmay(), cyclic_permutation=True) processor.add_control(tensor([sigmay(), sigmay()])) setting_args = {"SNOT": {"num_tslots": 10, "evo_time": 1}, "SWAP": {"num_tslots": 30, "evo_time": 3}, "CNOT": {"num_tslots": 30, "evo_time": 3}} tlist, coeffs = processor.load_circuit(qc, setting_args=setting_args, merge_gates=False) Parameters ---------- qc: :class:`.QubitCircuit` or list of Qobj The quantum circuit to be translated. min_fid_err: float, optional The minimal fidelity tolerance, if the fidelity error of any gate decomposition is higher, a warning will be given. Default is infinite. merge_gates: boolean, optimal If True, merge all gate/Qobj into one Qobj and then find the optimal pulses for this unitary matrix. If False, find the optimal pulses for each gate/Qobj. setting_args: dict, optional Only considered if merge_gates is False. It is a dictionary containing keyword arguments for different gates. E.g.: :: setting_args = {"SNOT": {"num_tslots": 10, "evo_time": 1}, "SWAP": {"num_tslots": 30, "evo_time": 3}, "CNOT": {"num_tslots": 30, "evo_time": 3}} verbose: boolean, optional If true, the information for each decomposed gate will be shown. Default is False. **kwargs keyword arguments for `qutip.control.optimize_pulse_unitary` Returns ------- tlist: array_like A NumPy array specifies the time of each coefficient coeffs: array_like A 2d NumPy array of the shape (len(ctrls), len(tlist)-1). Each row corresponds to the control pulse sequence for one Hamiltonian. Notes ----- ``len(tlist) - 1 = coeffs.shape[1]`` since tlist gives the beginning and the end of the pulses. """ if setting_args is None: setting_args = {} if isinstance(qc, QubitCircuit): props = qc.propagators() gates = [ for g in qc.gates] elif isinstance(qc, Iterable): props = qc gates = None # using list of Qobj, no gates name else: raise ValueError( "qc should be a " "QubitCircuit or a list of Qobj") if merge_gates: # merge all gates/Qobj into one Qobj props = [gate_sequence_product(props)] gates = None time_record = [] # a list for all the gates coeff_record = [] last_time = 0. # used in concatenation of tlist for prop_ind, U_targ in enumerate(props): U_0 = identity(U_targ.dims[0]) # If qc is a QubitCircuit and setting_args is not empty, # we update the kwargs for each gate. # keyword arguments in setting_arg have priority if gates is not None and setting_args: kwargs.update(setting_args[gates[prop_ind]]) full_drift_ham = self.drift.get_ideal_qobjevo(self.dims).cte full_ctrls_hams = [pulse.get_ideal_qobj(self.dims) for pulse in self.pulses] result = cpo.optimize_pulse_unitary( full_drift_ham, full_ctrls_hams, U_0, U_targ, **kwargs) if result.fid_err > min_fid_err: warnings.warn( "The fidelity error of gate {} is higher " "than required limit. Use verbose=True to see" "the more detailed information.".format(prop_ind)) time_record.append(result.time[1:] + last_time) last_time += result.time[-1] coeff_record.append(result.final_amps.T) if verbose: print("********** Gate {} **********".format(prop_ind)) print("Final fidelity error {}".format(result.fid_err)) print("Final gradient normal {}".format( result.grad_norm_final)) print("Terminated due to {}".format(result.termination_reason)) print("Number of iterations {}".format(result.num_iter)) tlist = np.hstack([[0.]] + time_record) for i in range(len(self.pulses)): self.pulses[i].tlist = tlist coeffs = np.vstack([np.hstack(coeff_record)]) self.coeffs = coeffs return tlist, coeffs