Source code for qiskit_experiments.library.tomography.qpt_experiment

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# (C) Copyright IBM 2021.
# This code is licensed under the Apache License, Version 2.0. You may
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Quantum Process Tomography experiment

from typing import Union, Optional, List, Tuple, Sequence
import numpy as np
from qiskit.circuit import QuantumCircuit, Instruction, Clbit
from qiskit.providers.backend import Backend
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit.quantum_info import Choi, Operator, Statevector, DensityMatrix, partial_trace

from qiskit_experiments.exceptions import QiskitError
from .tomography_experiment import TomographyExperiment, TomographyAnalysis, BaseAnalysis
from .qpt_analysis import ProcessTomographyAnalysis
from . import basis

[docs] class ProcessTomography(TomographyExperiment): """An experiment to reconstruct the quantum channel from measurement data. # section: overview Quantum process tomography (QPT) is a method for experimentally reconstructing the quantum channel from measurement data. A QPT experiment prepares multiple input states, evolves them by the circuit, then performs multiple measurements in different measurement bases. The resulting measurement data is then post-processed by a tomography fitter to reconstruct the quantum channel. # section: note Performing full process tomography on an `N`-qubit circuit requires running :math:`4^N 3^N` measurement circuits when using the default preparation and measurement bases. # section: analysis_ref :class:`ProcessTomographyAnalysis` """ def __init__( self, circuit: Union[QuantumCircuit, Instruction, BaseOperator], backend: Optional[Backend] = None, physical_qubits: Optional[Sequence[int]] = None, measurement_basis: basis.MeasurementBasis = basis.PauliMeasurementBasis(), measurement_indices: Optional[Sequence[int]] = None, preparation_basis: basis.PreparationBasis = basis.PauliPreparationBasis(), preparation_indices: Optional[Sequence[int]] = None, basis_indices: Optional[Sequence[Tuple[List[int], List[int]]]] = None, conditional_circuit_clbits: Union[bool, Sequence[int], Sequence[Clbit]] = False, analysis: Union[BaseAnalysis, None, str] = "default", target: Union[Statevector, DensityMatrix, None, str] = "default", ): """Initialize a quantum process tomography experiment. Args: circuit: the quantum process circuit. If not a quantum circuit it must be a class that can be appended to a quantum circuit. backend: The backend to run the experiment on. physical_qubits: Optional, the physical qubits for the initial state circuit. If None this will be qubits [0, N) for an N-qubit circuit. measurement_basis: Tomography basis for measurements. If not specified the default basis is the :class:`~basis.PauliMeasurementBasis`. measurement_indices: Optional, the `physical_qubits` indices to be measured. If None all circuit physical qubits will be measured. preparation_basis: Tomography basis for measurements. If not specified the default basis is the :class:`~basis.PauliPreparationBasis`. preparation_indices: Optional, the `physical_qubits` indices to be prepared. If None all circuit physical qubits will be prepared. basis_indices: Optional, a list of basis indices for generating partial tomography measurement data. Each item should be given as a pair of lists of preparation and measurement basis configurations ``([p[0], p[1], ...], [m[0], m[1], ...])``, where ``p[i]`` is the preparation basis index, and ``m[i]`` is the measurement basis index for qubit-i. If not specified full tomography for all indices of the preparation and measurement bases will be performed. conditional_circuit_clbits: Optional, the clbits in the source circuit to be conditioned on when reconstructing the channel. If True all circuit clbits will be conditioned on. Enabling this will return a list of reconstructed channel components conditional on the values of these clbit values. analysis: Optional, a custom analysis instance to use. If ``"default"`` :class:`~.ProcessTomographyAnalysis` will be used. If None no analysis instance will be set. target: Optional, a custom quantum state target for computing the state fidelity of the fitted density matrix during analysis. If "default" the state will be inferred from the input circuit if it contains no classical instructions. """ if analysis == "default": analysis = ProcessTomographyAnalysis() super().__init__( circuit, backend=backend, physical_qubits=physical_qubits, measurement_basis=measurement_basis, measurement_indices=measurement_indices, preparation_basis=preparation_basis, preparation_indices=preparation_indices, basis_indices=basis_indices, conditional_circuit_clbits=conditional_circuit_clbits, analysis=analysis, ) # Set target quantum channel if isinstance(self.analysis, TomographyAnalysis): if target == "default": target = self._target_quantum_channel() self.analysis.set_options(target=target) def _target_quantum_channel(self) -> Union[Choi, Operator]: """Return the process tomography target""" # Check if circuit contains measure instructions # If so we cannot return target state circuit_ops = self._circuit.count_ops() if "measure" in circuit_ops: return None try: circuit = self._permute_circuit() if "reset" in circuit_ops or "kraus" in circuit_ops or "superop" in circuit_ops: channel = Choi(circuit) else: channel = Operator(circuit) except QiskitError: # Circuit couldn't be simulated return None total_qubits = self._circuit.num_qubits num_meas = total_qubits if not self._meas_indices else len(self._meas_indices) num_prep = total_qubits if not self._prep_indices else len(self._prep_indices) # If all qubits are prepared or measurement we are done if num_meas == total_qubits and num_prep == total_qubits: return channel # Convert channel to a state to project and trace out non-tomography # input and output qubits if isinstance(channel, Operator): chan_state = Statevector(np.ravel(channel, order="F")) else: chan_state = DensityMatrix( # Get qargs for non measured and prepared subsystems non_meas_qargs = list(range(num_meas, total_qubits)) non_prep_qargs = list(range(total_qubits + num_prep, 2 * total_qubits)) # Project non-prepared subsystems on to the zero state if non_prep_qargs: proj0 = Operator([[1, 0], [0, 0]]) for qarg in non_prep_qargs: chan_state = chan_state.evolve(proj0, [qarg]) # Trace out indices to remove tr_qargs = non_meas_qargs + non_prep_qargs chan_state = partial_trace(chan_state, tr_qargs) channel = Choi(, input_dims=[2] * num_prep, output_dims=[2] * num_meas) return channel