Source code for pennylane._qubit_device

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module contains the :class:`QubitDevice` abstract base class.
"""

# For now, arguments may be different from the signatures provided in Device
# e.g. instead of expval(self, observable, wires, par) have expval(self, observable)
# pylint: disable=arguments-differ, abstract-method, no-value-for-parameter,too-many-instance-attributes,too-many-branches, no-member, bad-option-value, arguments-renamed
import abc
import itertools
import warnings

import numpy as np

import pennylane as qml
from pennylane import Device, DeviceError
from pennylane.interfaces import set_shots
from pennylane.math import multiply as qmlmul
from pennylane.math import sum as qmlsum
from pennylane.measurements import (
    AllCounts,
    Counts,
    Expectation,
    MeasurementProcess,
    MutualInfo,
    Probability,
    Sample,
    Shadow,
    ShadowExpval,
    State,
    Variance,
    VnEntropy,
)
from pennylane.operation import operation_derivative
from pennylane.wires import Wires


[docs]class QubitDevice(Device): """Abstract base class for PennyLane qubit devices. The following abstract method **must** be defined: * :meth:`~.apply`: append circuit operations, compile the circuit (if applicable), and perform the quantum computation. Devices that generate their own samples (such as hardware) may optionally overwrite :meth:`~.probabilty`. This method otherwise automatically computes the probabilities from the generated samples, and **must** overwrite the following method: * :meth:`~.generate_samples`: Generate samples from the device from the exact or approximate probability distribution. Analytic devices **must** overwrite the following method: * :meth:`~.analytic_probability`: returns the probability or marginal probability from the device after circuit execution. :meth:`~.marginal_prob` may be used here. This device contains common utility methods for qubit-based devices. These do not need to be overwritten. Utility methods include: * :meth:`~.expval`, :meth:`~.var`, :meth:`~.sample`: return expectation values, variances, and samples of observables after the circuit has been rotated into the observable eigenbasis. Args: wires (int, Iterable[Number, str]]): Number of subsystems represented by the device, or iterable that contains unique labels for the subsystems as numbers (i.e., ``[-1, 0, 2]``) or strings (``['ancilla', 'q1', 'q2']``). Default 1 if not specified. shots (None, int, list[int]): Number of circuit evaluations/random samples used to estimate expectation values of observables. If ``None``, the device calculates probability, expectation values, and variances analytically. If an integer, it specifies the number of samples to estimate these quantities. If a list of integers is passed, the circuit evaluations are batched over the list of shots. r_dtype: Real floating point precision type. c_dtype: Complex floating point precision type. """ # pylint: disable=too-many-public-methods _asarray = staticmethod(np.asarray) _dot = staticmethod(np.dot) _abs = staticmethod(np.abs) _reduce_sum = staticmethod(lambda array, axes: np.sum(array, axis=tuple(axes))) _reshape = staticmethod(np.reshape) _flatten = staticmethod(lambda array: array.flatten()) _gather = staticmethod( lambda array, indices, axis=0: array[:, indices] if axis == 1 else array[indices] ) # Make sure to only use _gather with axis=0 or axis=1 _einsum = staticmethod(np.einsum) _cast = staticmethod(np.asarray) _transpose = staticmethod(np.transpose) _tensordot = staticmethod(np.tensordot) _conj = staticmethod(np.conj) _imag = staticmethod(np.imag) _roll = staticmethod(np.roll) _stack = staticmethod(np.stack) _outer = staticmethod(np.outer) _diag = staticmethod(np.diag) _real = staticmethod(np.real) _size = staticmethod(np.size) _ndim = staticmethod(np.ndim) @staticmethod def _scatter(indices, array, new_dimensions): new_array = np.zeros(new_dimensions, dtype=array.dtype.type) new_array[indices] = array return new_array @staticmethod def _const_mul(constant, array): """Data type preserving multiply operation""" return qmlmul(constant, array, dtype=array.dtype) def _permute_wires(self, observable): r"""Given an observable which acts on multiple wires, permute the wires to be consistent with the device wire order. Suppose we are given an observable :math:`\hat{O} = \Identity \otimes \Identity \otimes \hat{Z}`. This observable can be represented in many ways: .. code-block:: python O_1 = qml.Identity(wires=0) @ qml.Identity(wires=1) @ qml.PauliZ(wires=2) O_2 = qml.PauliZ(wires=2) @ qml.Identity(wires=0) @ qml.Identity(wires=1) Notice that while the explicit tensor product matrix representation of :code:`O_1` and :code:`O_2` is different, the underlying operator is identical due to the wire labelling (assuming the labels in ascending order are {0,1,2}). If we wish to compute the expectation value of such an observable, we must ensure it is identical in both cases. To facilitate this, we permute the wires in our state vector such that they are consistent with this swapping of order in the tensor observable. .. code-block:: python >>> print(O_1.wires) <Wires = [0, 1, 2]> >>> print(O_2.wires) <Wires = [2, 0, 1]> We might naively think that we must permute our state vector to match the wire order of our tensor observable. We must be careful and realize that the wire order of the terms in the tensor observable DOES NOT match the permutation of the terms themselves. As an example we directly compare :code:`O_1` and :code:`O_2`: The first term in :code:`O_1` (:code:`qml.Identity(wires=0)`) became the second term in :code:`O_2`. By similar comparison we see that each term in the tensor product was shifted one position forward (i.e 0 --> 1, 1 --> 2, 2 --> 0). The wires in our permuted quantum state should follow their respective terms in the tensor product observable. Thus, the correct wire ordering should be :code:`permuted_wires = <Wires = [1, 2, 0]>`. But if we had taken the naive approach we would have permuted our state according to :code:`permuted_wires = <Wires = [2, 0, 1]>` which is NOT correct. This function uses the observable wires and the global device wire ordering in order to determine the permutation of the wires in the observable required such that if our quantum state vector is permuted accordingly then the amplitudes of the state will match the matrix representation of the observable. Args: observable (Observable): the observable whose wires are to be permuted. Returns: permuted_wires (Wires): permuted wires object """ ordered_obs_wire_lst = self.order_wires( observable.wires ).tolist() # order according to device wire order mapped_wires = self.map_wires(observable.wires) if isinstance(mapped_wires, Wires): # by default this should be a Wires obj, but it is overwritten to list object in default.qubit mapped_wires = mapped_wires.tolist() permutation = np.argsort(mapped_wires) # extract permutation via argsort permuted_wires = Wires([ordered_obs_wire_lst[index] for index in permutation]) return permuted_wires observables = { "PauliX", "PauliY", "PauliZ", "Hadamard", "Hermitian", "Identity", "Projector", "Sum", "Sprod", "Prod", } def __init__( self, wires=1, shots=None, *, r_dtype=np.float64, c_dtype=np.complex128, analytic=None ): super().__init__(wires=wires, shots=shots, analytic=analytic) if "float" not in str(r_dtype): raise DeviceError("Real datatype must be a floating point type.") if "complex" not in str(c_dtype): raise DeviceError("Complex datatype must be a complex floating point type.") self.C_DTYPE = c_dtype self.R_DTYPE = r_dtype self._samples = None """None or array[int]: stores the samples generated by the device *after* rotation to diagonalize the observables."""
[docs] @classmethod def capabilities(cls): capabilities = super().capabilities().copy() capabilities.update( model="qubit", supports_broadcasting=False, supports_finite_shots=True, supports_tensor_observables=True, returns_probs=True, ) return capabilities
[docs] def reset(self): """Reset the backend state. After the reset, the backend should be as if it was just constructed. Most importantly the quantum state is reset to its initial value. """ self._samples = None
def _collect_shotvector_results(self, circuit, counts_exist): """Obtain and process statistics when using a shot vector. This routine is part of the ``execute()`` method.""" if self._ndim(self._samples) == 3: raise NotImplementedError( "Parameter broadcasting when using a shot vector is not supported yet." ) results = [] s1 = 0 for shot_tuple in self._shot_vector: s2 = s1 + np.prod(shot_tuple) r = self.statistics(circuit.observables, shot_range=[s1, s2], bin_size=shot_tuple.shots) if qml.math.get_interface(*r) == "jax": # pylint: disable=protected-access r = r[0] elif not counts_exist: # Measurement types except for Counts r = qml.math.squeeze(r) if counts_exist: # This happens when at least one measurement type is Counts for result_group in r: if isinstance(result_group, list): # List that contains one or more dictionaries results.extend(result_group) else: # Other measurement results results.append(result_group.T) elif shot_tuple.copies > 1: results.extend(r.T) else: results.append(r.T) s1 = s2 multiple_sampled_jobs = circuit.is_sampled and self._has_partitioned_shots() if not multiple_sampled_jobs and not counts_exist: # Can only stack single element outputs results = self._stack(results) return results def _execute_new(self, circuit, **kwargs): """New execute (update of return type) function, it executes a queue of quantum operations on the device and then measure the given observables. More case will be added in future PRs, for the moment it only supports measurements without shots. For plugin developers: instead of overwriting this, consider implementing a suitable subset of * :meth:`apply` * :meth:`~.generate_samples` * :meth:`~.probability` Additional keyword arguments may be passed to the this method that can be utilised by :meth:`apply`. An example would be passing the ``QNode`` hash that can be used later for parametric compilation. Args: circuit (~.tapes.QuantumTape): circuit to execute on the device Raises: QuantumFunctionError: if the value of :attr:`~.Observable.return_type` is not supported Returns: array[float]: measured value(s) """ self.check_validity(circuit.operations, circuit.observables) # apply all circuit operations self.apply(circuit.operations, rotations=circuit.diagonalizing_gates, **kwargs) # generate computational basis samples if self.shots is not None: self._samples = self.generate_samples() # compute the required statistics if self._shot_vector is not None: results = self.shot_vec_statistics(circuit) else: results = self._statistics_new(circuit.observables) single_measurement = len(circuit.measurements) == 1 if single_measurement: results = results[0] else: results = tuple(results) # increment counter for number of executions of qubit device self._num_executions += 1 # if self.tracker.active: # self.tracker.update(executions=1, shots=self._shots) # self.tracker.record() return results
[docs] def execute(self, circuit, **kwargs): """Execute a queue of quantum operations on the device and then measure the given observables. For plugin developers: instead of overwriting this, consider implementing a suitable subset of * :meth:`apply` * :meth:`~.generate_samples` * :meth:`~.probability` Additional keyword arguments may be passed to the this method that can be utilised by :meth:`apply`. An example would be passing the ``QNode`` hash that can be used later for parametric compilation. Args: circuit (~.tapes.QuantumTape): circuit to execute on the device Raises: QuantumFunctionError: if the value of :attr:`~.Observable.return_type` is not supported Returns: array[float]: measured value(s) """ if qml.active_return(): return self._execute_new(circuit, **kwargs) self.check_validity(circuit.operations, circuit.observables) # apply all circuit operations self.apply(circuit.operations, rotations=circuit.diagonalizing_gates, **kwargs) # generate computational basis samples if self.shots is not None or circuit.is_sampled: self._samples = self.generate_samples() ret_types = [m.return_type for m in circuit.measurements] counts_exist = any( ret in (qml.measurements.Counts, qml.measurements.AllCounts) for ret in ret_types ) # compute the required statistics if not self.analytic and self._shot_vector is not None: results = self._collect_shotvector_results(circuit, counts_exist) else: results = self.statistics(circuit.observables, circuit=circuit) if not circuit.is_sampled: if len(circuit.measurements) == 1: if ret_types[0] is qml.measurements.State: # State: assumed to only be allowed if it's the only measurement results = self._asarray(results, dtype=self.C_DTYPE) else: # Measurements with expval, var or probs try: # Feature for returning custom objects: if the type cannot be cast to float then we can still allow it as an output results = self._asarray(results, dtype=self.R_DTYPE) except TypeError: pass elif all( ret in (qml.measurements.Expectation, qml.measurements.Variance) for ret in ret_types ): # Measurements with expval or var results = self._asarray(results, dtype=self.R_DTYPE) elif not counts_exist: # all the other cases except any counts results = self._asarray(results) elif circuit.all_sampled and not self._has_partitioned_shots() and not counts_exist: results = self._asarray(results) else: results = tuple( qml.math.squeeze(self._asarray(r)) if not isinstance(r, dict) else r for r in results ) # increment counter for number of executions of qubit device self._num_executions += 1 if self.tracker.active: self.tracker.update(executions=1, shots=self._shots) self.tracker.record() return results
[docs] def shot_vec_statistics(self, circuit): """Process measurement results from circuit execution using a device with a shot vector and return statistics. This is an auxiliary method of execute_new and uses statistics_new. When using shot vectors, measurement results for each item of the shot vector are contained in a tuple. Args: circuit (~.tapes.QuantumTape): circuit to execute on the device Raises: QuantumFunctionError: if the value of :attr:`~.Observable.return_type` is not supported Returns: tuple: stastics for each shot item from the shot vector """ results = [] s1 = 0 ret_types = [m.return_type for m in circuit.measurements] counts_exist = any( ret in (qml.measurements.Counts, qml.measurements.AllCounts) for ret in ret_types ) single_measurement = len(circuit.measurements) == 1 for shot_tuple in self._shot_vector: s2 = s1 + np.prod(shot_tuple) r = self._statistics_new( circuit.observables, shot_range=[s1, s2], bin_size=shot_tuple.shots ) # This will likely be required: # if qml.math.get_interface(*r) == "jax": # pylint: disable=protected-access # r = r[0] if single_measurement: r = r[0] else: if shot_tuple.copies == 1: r = tuple( r_[0] if isinstance(r_, list) else r_.T # need to unwrap the single element for idx, r_ in enumerate(r) ) else: if counts_exist: r = self._multi_meas_with_counts_shot_vec(circuit, shot_tuple, r) else: # r is a nested sequence, contains the results for # multiple measurements # # Each item of r has copies length, we need to extract # each measurement result from the arrays # 1. transpose: applied because measurements like probs # for multiple copies output results with shape (N, # copies) and we'd like to index straight to get rows # which requires a shape of (copies, N) # 2. asarray: done because indexing into a flat array produces a # scalar instead of a scalar shaped array r = [ tuple(self._asarray(r_.T[idx]) for r_ in r) for idx in range(shot_tuple.copies) ] if isinstance(r, qml.numpy.ndarray): if shot_tuple.copies > 1: results.extend(r.T) else: results.append(r.T) else: if single_measurement and counts_exist: # Results are nested in a sequence results.extend(r) elif not single_measurement and shot_tuple.copies > 1: # Some samples may still be transposed, fix their shapes # Leave dictionaries intact r = [ tuple(elem.T if not isinstance(elem, dict) else elem for elem in r_) for r_ in r ] results.extend(r) else: results.append(r) s1 = s2 results = tuple(results) return results
def _multi_meas_with_counts_shot_vec(self, circuit, shot_tuple, r): """Auxiliary function of the shot_vec_statistics and execute_new functions for post-processing the results of multiple measurements at least one of which was a counts measurement. The measurements were executed on a device that defines a shot vector. """ # First: iterate over each group of measurement # results that contain copies many outcomes for a # single measurement new_r = [] # Each item of r has copies length for idx in range(shot_tuple.copies): result_group = [] for idx2, r_ in enumerate(r): measurement_proc = circuit.measurements[idx2] if measurement_proc.return_type is Probability or ( measurement_proc.return_type is Sample and measurement_proc.obs ): # Here, the result has a shape of (num_basis_states, shot_tuple.copies) # Extract a single row -> shape (num_basis_states,) result = r_[:, idx] else: result = r_[idx] if not circuit.observables[idx2].return_type in (Counts, AllCounts): result = self._asarray(result.T) result_group.append(result) new_r.append(tuple(result_group)) return new_r def _batch_execute_new(self, circuits): """Temporary batch execute function, waiting for QNode execution of the new return types. Execute a batch of quantum circuits on the device. The circuits are represented by tapes, and they are executed one-by-one using the device's ``execute`` method. The results are collected in a list. For plugin developers: This function should be overwritten if the device can efficiently run multiple circuits on a backend, for example using parallel and/or asynchronous executions. Args: circuits (list[.tapes.QuantumTape]): circuits to execute on the device Returns: list[array[float]]: list of measured value(s) """ # TODO: This method and the tests can be globally implemented by Device # once it has the same signature in the execute() method results = [] for circuit in circuits: # we need to reset the device here, else it will # not start the next computation in the zero state self.reset() res = self._execute_new(circuit) results.append(res) # if self.tracker.active: # self.tracker.update(batches=1, batch_len=len(circuits)) # self.tracker.record() return results
[docs] def batch_execute(self, circuits): """Execute a batch of quantum circuits on the device. The circuits are represented by tapes, and they are executed one-by-one using the device's ``execute`` method. The results are collected in a list. For plugin developers: This function should be overwritten if the device can efficiently run multiple circuits on a backend, for example using parallel and/or asynchronous executions. Args: circuits (list[.tapes.QuantumTape]): circuits to execute on the device Returns: list[array[float]]: list of measured value(s) """ # TODO: This method and the tests can be globally implemented by Device # once it has the same signature in the execute() method if qml.active_return(): return self._batch_execute_new(circuits=circuits) results = [] for circuit in circuits: # we need to reset the device here, else it will # not start the next computation in the zero state self.reset() # TODO: Insert control on value here res = self.execute(circuit) results.append(res) if self.tracker.active: self.tracker.update(batches=1, batch_len=len(circuits)) self.tracker.record() return results
[docs] @abc.abstractmethod def apply(self, operations, **kwargs): """Apply quantum operations, rotate the circuit into the measurement basis, and compile and execute the quantum circuit. This method receives a list of quantum operations queued by the QNode, and should be responsible for: * Constructing the quantum program * (Optional) Rotating the quantum circuit using the rotation operations provided. This diagonalizes the circuit so that arbitrary observables can be measured in the computational basis. * Compile the circuit * Execute the quantum circuit Both arguments are provided as lists of PennyLane :class:`~.Operation` instances. Useful properties include :attr:`~.Operation.name`, :attr:`~.Operation.wires`, and :attr:`~.Operation.parameters`, and :attr:`~.Operation.inverse`: >>> op = qml.RX(0.2, wires=[0]) >>> op.name # returns the operation name "RX" >>> op.wires # returns a Wires object representing the wires that the operation acts on <Wires = [0]> >>> op.parameters # returns a list of parameters [0.2] >>> op.inverse # check if the operation should be inverted False >>> op = qml.RX(0.2, wires=[0]).inv >>> op.inverse True Args: operations (list[~.Operation]): operations to apply to the device Keyword args: rotations (list[~.Operation]): operations that rotate the circuit pre-measurement into the eigenbasis of the observables. hash (int): the hash value of the circuit constructed by `CircuitGraph.hash` """
[docs] @staticmethod def active_wires(operators): """Returns the wires acted on by a set of operators. Args: operators (list[~.Operation]): operators for which we are gathering the active wires Returns: Wires: wires activated by the specified operators """ list_of_wires = [op.wires for op in operators] return Wires.all_wires(list_of_wires)
[docs] def statistics(self, observables, shot_range=None, bin_size=None, circuit=None): """Process measurement results from circuit execution and return statistics. This includes returning expectation values, variance, samples, probabilities, states, and density matrices. Args: observables (List[.Observable]): the observables to be measured shot_range (tuple[int]): 2-tuple of integers specifying the range of samples to use. If not specified, all samples are used. bin_size (int): Divides the shot range into bins of size ``bin_size``, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin. circuit (~.tape.QuantumTape): the quantum tape currently being executed Raises: QuantumFunctionError: if the value of :attr:`~.Observable.return_type` is not supported Returns: Union[float, List[float]]: the corresponding statistics .. details:: :title: Usage Details The ``shot_range`` and ``bin_size`` arguments allow for the statistics to be performed on only a subset of device samples. This finer level of control is accessible from the main UI by instantiating a device with a batch of shots. For example, consider the following device: >>> dev = qml.device("my_device", shots=[5, (10, 3), 100]) This device will execute QNodes using 135 shots, however measurement statistics will be **coarse grained** across these 135 shots: * All measurement statistics will first be computed using the first 5 shots --- that is, ``shots_range=[0, 5]``, ``bin_size=5``. * Next, the tuple ``(10, 3)`` indicates 10 shots, repeated 3 times. We will want to use ``shot_range=[5, 35]``, performing the expectation value in bins of size 10 (``bin_size=10``). * Finally, we repeat the measurement statistics for the final 100 shots, ``shot_range=[35, 135]``, ``bin_size=100``. """ results = [] for obs in observables: # Pass instances directly if obs.return_type is Expectation: # Appends a result of shape (num_bins,) if bin_size is not None, else a scalar results.append(self.expval(obs, shot_range=shot_range, bin_size=bin_size)) elif obs.return_type is Variance: # Appends a result of shape (num_bins,) if bin_size is not None, else a scalar results.append(self.var(obs, shot_range=shot_range, bin_size=bin_size)) elif obs.return_type is Sample: # Appends a result of shape (shots, num_bins,) if bin_size is not None else (shots,) results.append( self.sample(obs, shot_range=shot_range, bin_size=bin_size, counts=False) ) elif obs.return_type in (Counts, AllCounts): results.append( self.sample(obs, shot_range=shot_range, bin_size=bin_size, counts=True) ) elif obs.return_type is Probability: # Appends a result of shape (2**len(obs.wires), num_bins,) # if bin_size is not None else (2**len(obs.wires),) results.append( self.probability(wires=obs.wires, shot_range=shot_range, bin_size=bin_size) ) elif obs.return_type is State: if len(observables) > 1: raise qml.QuantumFunctionError( "The state or density matrix cannot be returned in combination" " with other return types" ) if self.shots is not None: warnings.warn( "Requested state or density matrix with finite shots; the returned " "state information is analytic and is unaffected by sampling. To silence " "this warning, set shots=None on the device.", UserWarning, ) # Check if the state is accessible and decide to return the state or the density # matrix. results.append(self.access_state(wires=obs.wires)) elif obs.return_type is VnEntropy: if self.wires.labels != tuple(range(self.num_wires)): raise qml.QuantumFunctionError( "Returning the Von Neumann entropy is not supported when using custom wire labels" ) if self._shot_vector is not None: raise NotImplementedError( "Returning the Von Neumann entropy is not supported with shot vectors." ) if self.shots is not None: warnings.warn( "Requested Von Neumann entropy with finite shots; the returned " "result is analytic and is unaffected by sampling. To silence " "this warning, set shots=None on the device.", UserWarning, ) results.append(self.vn_entropy(wires=obs.wires, log_base=obs.log_base)) elif obs.return_type is MutualInfo: if self.wires.labels != tuple(range(self.num_wires)): raise qml.QuantumFunctionError( "Returning the mutual information is not supported when using custom wire labels" ) if self._shot_vector is not None: raise NotImplementedError( "Returning the mutual information is not supported with shot vectors." ) if self.shots is not None: warnings.warn( "Requested mutual information with finite shots; the returned " "state information is analytic and is unaffected by sampling. To silence " "this warning, set shots=None on the device.", UserWarning, ) wires0, wires1 = obs.raw_wires results.append( self.mutual_info(wires0=wires0, wires1=wires1, log_base=obs.log_base) ) elif obs.return_type is Shadow: if len(observables) > 1: raise qml.QuantumFunctionError( "Classical shadows cannot be returned in combination" " with other return types" ) results.append(self.classical_shadow(obs, circuit=circuit)) elif obs.return_type is ShadowExpval: if len(observables) > 1: raise qml.QuantumFunctionError( "Classical shadows cannot be returned in combination" " with other return types" ) results.append(self.shadow_expval(obs, circuit=circuit)) elif obs.return_type is not None: raise qml.QuantumFunctionError( f"Unsupported return type specified for observable {obs.name}" ) return results
def _statistics_new(self, observables, shot_range=None, bin_size=None): """Process measurement results from circuit execution and return statistics. This includes returning expectation values, variance, samples, probabilities, states, and density matrices. Args: observables (List[.Observable]): the observables to be measured shot_range (tuple[int]): 2-tuple of integers specifying the range of samples to use. If not specified, all samples are used. bin_size (int): Divides the shot range into bins of size ``bin_size``, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin. Raises: QuantumFunctionError: if the value of :attr:`~.Observable.return_type` is not supported Returns: Union[float, List[float]]: the corresponding statistics .. details:: :title: Usage Details The ``shot_range`` and ``bin_size`` arguments allow for the statistics to be performed on only a subset of device samples. This finer level of control is accessible from the main UI by instantiating a device with a batch of shots. For example, consider the following device: >>> dev = qml.device("my_device", shots=[5, (10, 3), 100]) This device will execute QNodes using 135 shots, however measurement statistics will be **course grained** across these 135 shots: * All measurement statistics will first be computed using the first 5 shots --- that is, ``shots_range=[0, 5]``, ``bin_size=5``. * Next, the tuple ``(10, 3)`` indicates 10 shots, repeated 3 times. We will want to use ``shot_range=[5, 35]``, performing the expectation value in bins of size 10 (``bin_size=10``). * Finally, we repeat the measurement statistics for the final 100 shots, ``shot_range=[35, 135]``, ``bin_size=100``. """ results = [] for obs in observables: # 1. Based on the return_type, compute statistics # Pass instances directly if obs.return_type is Expectation: result = self.expval(obs, shot_range=shot_range, bin_size=bin_size) elif obs.return_type is Variance: result = self.var(obs, shot_range=shot_range, bin_size=bin_size) elif obs.return_type is Sample: samples = self.sample(obs, shot_range=shot_range, bin_size=bin_size, counts=False) result = self._asarray(qml.math.squeeze(samples)) elif obs.return_type in (Counts, AllCounts): result = self.sample(obs, shot_range=shot_range, bin_size=bin_size, counts=True) elif obs.return_type is Probability: result = self.probability(wires=obs.wires, shot_range=shot_range, bin_size=bin_size) elif obs.return_type is State: if len(observables) > 1: raise qml.QuantumFunctionError( "The state or density matrix cannot be returned in combination" " with other return types" ) if self.shots is not None: warnings.warn( "Requested state or density matrix with finite shots; the returned " "state information is analytic and is unaffected by sampling. To silence " "this warning, set shots=None on the device.", UserWarning, ) # Check if the state is accessible and decide to return the state or the density # matrix. state = self.access_state(wires=obs.wires) result = self._asarray(state, dtype=self.C_DTYPE) elif obs.return_type is VnEntropy: if self.wires.labels != tuple(range(self.num_wires)): raise qml.QuantumFunctionError( "Returning the Von Neumann entropy is not supported when using custom wire labels" ) # TODO: qml.execute shot vec support required with new return types # if self._shot_vector is not None: # raise NotImplementedError( # "Returning the Von Neumann entropy is not supported with shot vectors." # ) if self.shots is not None: warnings.warn( "Requested Von Neumann entropy with finite shots; the returned " "result is analytic and is unaffected by sampling. To silence " "this warning, set shots=None on the device.", UserWarning, ) result = self.vn_entropy(wires=obs.wires, log_base=obs.log_base) elif obs.return_type is MutualInfo: if self.wires.labels != tuple(range(self.num_wires)): raise qml.QuantumFunctionError( "Returning the mutual information is not supported when using custom wire labels" ) # TODO: qml.execute shot vec support required with new return types # if self._shot_vector is not None: # raise NotImplementedError( # "Returning the mutual information is not supported with shot vectors." # ) if self.shots is not None: warnings.warn( "Requested mutual information with finite shots; the returned " "state information is analytic and is unaffected by sampling. To silence " "this warning, set shots=None on the device.", UserWarning, ) wires0, wires1 = obs.raw_wires result = self.mutual_info(wires0=wires0, wires1=wires1, log_base=obs.log_base) elif obs.return_type is not None: raise qml.QuantumFunctionError( f"Unsupported return type specified for observable {obs.name}" ) # 2. Post-process statistics results (if need be) float_return_types = {Expectation, Variance, Probability, VnEntropy, MutualInfo} if obs.return_type in float_return_types: result = self._asarray(result, dtype=self.R_DTYPE) if self._shot_vector is not None and isinstance(result, np.ndarray): # In the shot vector case, measurement results may be of shape (N, 1) instead of (N,) # Squeeze the result to transform the results # # E.g., # before: # [[0.489] # [0.511] # [0. ] # [0. ]] # # after: [0.489 0.511 0. 0. ] result = qml.math.squeeze(result) # 3. Append to final list results.append(result) return results
[docs] def access_state(self, wires=None): """Check that the device has access to an internal state and return it if available. Args: wires (Wires): wires of the reduced system Raises: QuantumFunctionError: if the device is not capable of returning the state Returns: array or tensor: the state or the density matrix of the device """ if not self.capabilities().get("returns_state"): raise qml.QuantumFunctionError( "The current device is not capable of returning the state" ) state = getattr(self, "state", None) if state is None: raise qml.QuantumFunctionError("The state is not available in the current device") if wires: density_matrix = self.density_matrix(wires) return density_matrix return state
[docs] def generate_samples(self): r"""Returns the computational basis samples generated for all wires. Note that PennyLane uses the convention :math:`|q_0,q_1,\dots,q_{N-1}\rangle` where :math:`q_0` is the most significant bit. .. warning:: This method should be overwritten on devices that generate their own computational basis samples, with the resulting computational basis samples stored as ``self._samples``. Returns: array[complex]: array of samples in the shape ``(dev.shots, dev.num_wires)`` """ number_of_states = 2**self.num_wires rotated_prob = self.analytic_probability() samples = self.sample_basis_states(number_of_states, rotated_prob) return self.states_to_binary(samples, self.num_wires)
[docs] def sample_basis_states(self, number_of_states, state_probability): """Sample from the computational basis states based on the state probability. This is an auxiliary method to the generate_samples method. Args: number_of_states (int): the number of basis states to sample from state_probability (array[float]): the computational basis probability vector Returns: array[int]: the sampled basis states """ if self.shots is None: raise qml.QuantumFunctionError( "The number of shots has to be explicitly set on the device " "when using sample-based measurements." ) shots = self.shots basis_states = np.arange(number_of_states) if self._ndim(state_probability) == 2: # np.random.choice does not support broadcasting as needed here. return np.array( [np.random.choice(basis_states, shots, p=prob) for prob in state_probability] ) return np.random.choice(basis_states, shots, p=state_probability)
[docs] def generate_basis_states(self, num_wires, dtype=np.uint32): """ Generates basis states in binary representation according to the number of wires specified. The states_to_binary method creates basis states faster (for larger systems at times over x25 times faster) than the approach using ``itertools.product``, at the expense of using slightly more memory. Due to the large size of the integer arrays for more than 32 bits, memory allocation errors may arise in the states_to_binary method. Hence we constraint the dtype of the array to represent unsigned integers on 32 bits. Due to this constraint, an overflow occurs for 32 or more wires, therefore this approach is used only for fewer wires. For smaller number of wires speed is comparable to the next approach (using ``itertools.product``), hence we resort to that one for testing purposes. Args: num_wires (int): the number wires dtype=np.uint32 (type): the data type of the arrays to use Returns: array[int]: the sampled basis states """ if 2 < num_wires < 32: states_base_ten = np.arange(2**num_wires, dtype=dtype) return self.states_to_binary(states_base_ten, num_wires, dtype=dtype) # A slower, but less memory intensive method basis_states_generator = itertools.product((0, 1), repeat=num_wires) return np.fromiter(itertools.chain(*basis_states_generator), dtype=int).reshape( -1, num_wires )
[docs] @staticmethod def states_to_binary(samples, num_wires, dtype=np.int64): """Convert basis states from base 10 to binary representation. This is an auxiliary method to the generate_samples method. Args: samples (array[int]): samples of basis states in base 10 representation num_wires (int): the number of qubits dtype (type): Type of the internal integer array to be used. Can be important to specify for large systems for memory allocation purposes. Returns: array[int]: basis states in binary representation """ powers_of_two = 1 << np.arange(num_wires, dtype=dtype) # `samples` typically is one-dimensional, but can be two-dimensional with broadcasting. # In any case we want to append a new axis at the *end* of the shape. states_sampled_base_ten = samples[..., None] & powers_of_two # `states_sampled_base_ten` can be two- or three-dimensional. We revert the *last* axis. return (states_sampled_base_ten > 0).astype(dtype)[..., ::-1]
@property def circuit_hash(self): """The hash of the circuit upon the last execution. This can be used by devices in :meth:`~.apply` for parametric compilation. """ raise NotImplementedError @property def state(self): """Returns the state vector of the circuit prior to measurement. .. note:: Only state vector simulators support this property. Please see the plugin documentation for more details. """ raise NotImplementedError
[docs] def density_matrix(self, wires): """Returns the reduced density matrix over the given wires. Args: wires (Wires): wires of the reduced system Returns: array[complex]: complex array of shape ``(2 ** len(wires), 2 ** len(wires))`` representing the reduced density matrix of the state prior to measurement. """ state = getattr(self, "state", None) wires = self.map_wires(wires) return qml.math.reduced_dm(state, indices=wires, c_dtype=self.C_DTYPE)
[docs] def vn_entropy(self, wires, log_base): r"""Returns the Von Neumann entropy prior to measurement. .. math:: S( \rho ) = -\text{Tr}( \rho \log ( \rho )) Args: wires (Wires): Wires of the considered subsystem. log_base (float): Base for the logarithm, default is None the natural logarithm is used in this case. Returns: float: returns the Von Neumann entropy """ try: state = self.access_state() except qml.QuantumFunctionError as e: # pragma: no cover raise NotImplementedError( f"Cannot compute the Von Neumman entropy with device {self.name} that is not capable of returning the " f"state. " ) from e wires = wires.tolist() return qml.math.vn_entropy(state, indices=wires, c_dtype=self.C_DTYPE, base=log_base)
[docs] def mutual_info(self, wires0, wires1, log_base): r"""Returns the mutual information prior to measurement: .. math:: I(A, B) = S(\rho^A) + S(\rho^B) - S(\rho^{AB}) where :math:`S` is the von Neumann entropy. Args: wires0 (Wires): wires of the first subsystem wires1 (Wires): wires of the second subsystem log_base (float): base to use in the logarithm Returns: float: the mutual information """ try: state = self.access_state() except qml.QuantumFunctionError as e: # pragma: no cover raise NotImplementedError( f"Cannot compute the mutual information with device {self.name} that is not capable of returning the " f"state. " ) from e wires0 = wires0.tolist() wires1 = wires1.tolist() return qml.math.mutual_info( state, indices0=wires0, indices1=wires1, c_dtype=self.C_DTYPE, base=log_base )
[docs] def classical_shadow(self, obs, circuit): """ Returns the measured bits and recipes in the classical shadow protocol. The protocol is described in detail in the `classical shadows paper <https://arxiv.org/abs/2002.08953>`_. This measurement process returns the randomized Pauli measurements (the ``recipes``) that are performed for each qubit and snapshot as an integer: - 0 for Pauli X, - 1 for Pauli Y, and - 2 for Pauli Z. It also returns the measurement results (the ``bits``); 0 if the 1 eigenvalue is sampled, and 1 if the -1 eigenvalue is sampled. The device shots are used to specify the number of snapshots. If ``T`` is the number of shots and ``n`` is the number of qubits, then both the measured bits and the Pauli measurements have shape ``(T, n)``. This implementation is device-agnostic and works by executing single-shot tapes containing randomized Pauli observables. Devices should override this if they can offer cleaner or faster implementations. .. seealso:: :func:`~.classical_shadow` Args: obs (~.pennylane.measurements.ShadowMeasurementProcess): The classical shadow measurement process circuit (~.tapes.QuantumTape): The quantum tape that is being executed Returns: tensor_like[int]: A tensor with shape ``(2, T, n)``, where the first row represents the measured bits and the second represents the recipes used. """ if circuit is None: # pragma: no cover raise ValueError("Circuit must be provided when measuring classical shadows") wires = obs.wires n_snapshots = self.shots seed = obs.seed with set_shots(self, shots=1): # slow implementation but works for all devices n_qubits = len(wires) mapped_wires = np.array(self.map_wires(wires)) if seed is not None: # seed the random measurement generation so that recipes # are the same for different executions with the same seed rng = np.random.RandomState(seed) recipes = rng.randint(0, 3, size=(n_snapshots, n_qubits)) else: recipes = np.random.randint(0, 3, size=(n_snapshots, n_qubits)) obs_list = [qml.PauliX, qml.PauliY, qml.PauliZ] outcomes = np.zeros((n_snapshots, n_qubits)) for t in range(n_snapshots): # compute rotations for the Pauli measurements rotations = [ rot for wire_idx, wire in enumerate(wires) for rot in obs_list[recipes[t][wire_idx]].compute_diagonalizing_gates( wires=wire ) ] self.reset() self.apply(circuit.operations, rotations=circuit.diagonalizing_gates + rotations) outcomes[t] = self.generate_samples()[0][mapped_wires] return self._cast(self._stack([outcomes, recipes]), dtype=np.int8)
[docs] def shadow_expval(self, obs, circuit): r"""Compute expectation values using classical shadows in a differentiable manner. Please refer to :func:`~.pennylane.shadow_expval` for detailed documentation. Args: obs (~.pennylane.measurements.ShadowMeasurementProcess): The classical shadow expectation value measurement process circuit (~.tapes.QuantumTape): The quantum tape that is being executed Returns: float: expectation value estimate. """ bits, recipes = self.classical_shadow(obs, circuit) shadow = qml.shadows.ClassicalShadow(bits, recipes, wire_map=obs.wires.tolist()) return shadow.expval(obs.H, obs.k)
[docs] def analytic_probability(self, wires=None): r"""Return the (marginal) probability of each computational basis state from the last run of the device. PennyLane uses the convention :math:`|q_0,q_1,\dots,q_{N-1}\rangle` where :math:`q_0` is the most significant bit. If no wires are specified, then all the basis states representable by the device are considered and no marginalization takes place. .. note:: :meth:`marginal_prob` may be used as a utility method to calculate the marginal probability distribution. Args: wires (Iterable[Number, str], Number, str, Wires): wires to return marginal probabilities for. Wires not provided are traced out of the system. Returns: array[float]: list of the probabilities """ raise NotImplementedError
[docs] def estimate_probability(self, wires=None, shot_range=None, bin_size=None): """Return the estimated probability of each computational basis state using the generated samples. Args: wires (Iterable[Number, str], Number, str, Wires): wires to calculate marginal probabilities for. Wires not provided are traced out of the system. shot_range (tuple[int]): 2-tuple of integers specifying the range of samples to use. If not specified, all samples are used. bin_size (int): Divides the shot range into bins of size ``bin_size``, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin. Returns: array[float]: list of the probabilities """ wires = wires or self.wires # convert to a Wires object wires = Wires(wires) # translate to wire labels used by device device_wires = self.map_wires(wires) num_wires = len(device_wires) if shot_range is None: # The Ellipsis (...) corresponds to broadcasting and shots dimensions or only shots samples = self._samples[..., device_wires] else: # The Ellipsis (...) corresponds to the broadcasting dimension or no axis at all samples = self._samples[..., slice(*shot_range), device_wires] # convert samples from a list of 0, 1 integers, to base 10 representation powers_of_two = 2 ** np.arange(num_wires)[::-1] indices = samples @ powers_of_two # `self._samples` typically has two axes ((shots, wires)) but can also have three with # broadcasting ((batch_size, shots, wires)) so that we simply read out the batch_size. batch_size = self._samples.shape[0] if np.ndim(self._samples) == 3 else None dim = 2**num_wires # count the basis state occurrences, and construct the probability vector if bin_size is not None: num_bins = samples.shape[-2] // bin_size prob = self._count_binned_samples(indices, batch_size, dim, bin_size, num_bins) else: prob = self._count_unbinned_samples(indices, batch_size, dim) return self._asarray(prob, dtype=self.R_DTYPE)
@staticmethod def _count_unbinned_samples(indices, batch_size, dim): """Count the occurences of sampled indices and convert them to relative counts in order to estimate their occurence probability.""" if batch_size is None: prob = np.zeros(dim, dtype=np.float64) basis_states, counts = np.unique(indices, return_counts=True) prob[basis_states] = counts / len(indices) return prob prob = np.zeros((batch_size, dim), dtype=np.float64) for i, idx in enumerate(indices): # iterate over the broadcasting dimension basis_states, counts = np.unique(idx, return_counts=True) prob[i, basis_states] = counts / len(idx) return prob @staticmethod def _count_binned_samples(indices, batch_size, dim, bin_size, num_bins): """Count the occurences of bins of sampled indices and convert them to relative counts in order to estimate their occurence probability per bin.""" if batch_size is None: prob = np.zeros((dim, num_bins), dtype=np.float64) indices = indices.reshape((num_bins, bin_size)) # count the basis state occurrences, and construct the probability vector for each bin for b, idx in enumerate(indices): basis_states, counts = np.unique(idx, return_counts=True) prob[basis_states, b] = counts / bin_size return prob prob = np.zeros((batch_size, dim, num_bins), dtype=np.float64) indices = indices.reshape((batch_size, num_bins, bin_size)) # count the basis state occurrences, and construct the probability vector # for each bin and broadcasting index for i, _indices in enumerate(indices): # First iterate over broadcasting dimension for b, idx in enumerate(_indices): # Then iterate over bins dimension basis_states, counts = np.unique(idx, return_counts=True) prob[i, basis_states, b] = counts / bin_size return prob
[docs] def probability(self, wires=None, shot_range=None, bin_size=None): """Return either the analytic probability or estimated probability of each computational basis state. Devices that require a finite number of shots always return the estimated probability. Args: wires (Iterable[Number, str], Number, str, Wires): wires to return marginal probabilities for. Wires not provided are traced out of the system. Returns: array[float]: list of the probabilities """ if self.shots is None: return self.analytic_probability(wires=wires) return self.estimate_probability(wires=wires, shot_range=shot_range, bin_size=bin_size)
@staticmethod def _get_batch_size(tensor, expected_shape, expected_size): """Determine whether a tensor has an additional batch dimension for broadcasting, compared to an expected_shape. As QubitDevice does not natively support broadcasting, it always reports no batch size, that is ``batch_size=None``""" # pylint: disable=unused-argument return None
[docs] def marginal_prob(self, prob, wires=None): r"""Return the marginal probability of the computational basis states by summing the probabiliites on the non-specified wires. If no wires are specified, then all the basis states representable by the device are considered and no marginalization takes place. .. note:: If the provided wires are not in the order as they appear on the device, the returned marginal probabilities take this permutation into account. For example, if the addressable wires on this device are ``Wires([0, 1, 2])`` and this function gets passed ``wires=[2, 0]``, then the returned marginal probability vector will take this 'reversal' of the two wires into account: .. math:: \mathbb{P}^{(2, 0)} = \left[ |00\rangle, |10\rangle, |01\rangle, |11\rangle \right] Args: prob: The probabilities to return the marginal probabilities for wires (Iterable[Number, str], Number, str, Wires): wires to return marginal probabilities for. Wires not provided are traced out of the system. Returns: array[float]: array of the resulting marginal probabilities. """ dim = 2**self.num_wires batch_size = self._get_batch_size(prob, (dim,), dim) # pylint: disable=assignment-from-none if wires is None: # no need to marginalize return prob wires = Wires(wires) # determine which subsystems are to be summed over inactive_wires = Wires.unique_wires([self.wires, wires]) # translate to wire labels used by device device_wires = self.map_wires(wires) inactive_device_wires = self.map_wires(inactive_wires) # reshape the probability so that each axis corresponds to a wire shape = [2] * self.num_wires if batch_size is not None: shape.insert(0, batch_size) # prob now is reshaped to have self.num_wires+1 axes in the case of broadcasting prob = self._reshape(prob, shape) # sum over all inactive wires # hotfix to catch when default.qubit uses this method # since then device_wires is a list if isinstance(inactive_device_wires, Wires): inactive_device_wires = inactive_device_wires.labels if batch_size is not None: inactive_device_wires = [idx + 1 for idx in inactive_device_wires] flat_shape = (-1,) if batch_size is None else (batch_size, -1) prob = self._reshape(self._reduce_sum(prob, inactive_device_wires), flat_shape) # The wires provided might not be in consecutive order (i.e., wires might be [2, 0]). # If this is the case, we must permute the marginalized probability so that # it corresponds to the orders of the wires passed. num_wires = len(device_wires) basis_states = self.generate_basis_states(num_wires) basis_states = basis_states[:, np.argsort(np.argsort(device_wires))] powers_of_two = 2 ** np.arange(len(device_wires))[::-1] perm = basis_states @ powers_of_two # The permutation happens on the last axis both with and without broadcasting out = self._gather(prob, perm, axis=1 if batch_size is not None else 0) return out
[docs] def expval(self, observable, shot_range=None, bin_size=None): if observable.name == "Projector": # branch specifically to handle the projector observable idx = int("".join(str(i) for i in observable.parameters[0]), 2) probs = self.probability( wires=observable.wires, shot_range=shot_range, bin_size=bin_size ) return probs[idx] # exact expectation value if self.shots is None: try: eigvals = self._asarray(observable.eigvals(), dtype=self.R_DTYPE) except qml.operation.EigvalsUndefinedError as e: raise qml.operation.EigvalsUndefinedError( f"Cannot compute analytic expectations of {observable.name}." ) from e # the probability vector must be permuted to account for the permuted # wire order of the observable permuted_wires = self._permute_wires(observable) prob = self.probability(wires=permuted_wires) # In case of broadcasting, `prob` has two axes and this is a matrix-vector product return self._dot(prob, eigvals) # estimate the ev samples = self.sample(observable, shot_range=shot_range, bin_size=bin_size) # With broadcasting, we want to take the mean over axis 1, which is the -1st/-2nd with/ # without bin_size. Without broadcasting, axis 0 is the -1st/-2nd with/without bin_size axis = -1 if bin_size is None else -2 # TODO: do we need to squeeze here? Maybe remove with new return types return np.squeeze(np.mean(samples, axis=axis))
[docs] def var(self, observable, shot_range=None, bin_size=None): if observable.name == "Projector": # branch specifically to handle the projector observable idx = int("".join(str(i) for i in observable.parameters[0]), 2) probs = self.probability( wires=observable.wires, shot_range=shot_range, bin_size=bin_size ) return probs[idx] - probs[idx] ** 2 # exact variance value if self.shots is None: try: eigvals = self._asarray(observable.eigvals(), dtype=self.R_DTYPE) except qml.operation.EigvalsUndefinedError as e: # if observable has no info on eigenvalues, we cannot return this measurement raise qml.operation.EigvalsUndefinedError( f"Cannot compute analytic variance of {observable.name}." ) from e # the probability vector must be permuted to account for the permuted wire order of the observable permuted_wires = self._permute_wires(observable) prob = self.probability(wires=permuted_wires) # In case of broadcasting, `prob` has two axes and these are a matrix-vector products return self._dot(prob, (eigvals**2)) - self._dot(prob, eigvals) ** 2 # estimate the variance samples = self.sample(observable, shot_range=shot_range, bin_size=bin_size) # With broadcasting, we want to take the variance over axis 1, which is the -1st/-2nd with/ # without bin_size. Without broadcasting, axis 0 is the -1st/-2nd with/without bin_size axis = -1 if bin_size is None else -2 # TODO: do we need to squeeze here? Maybe remove with new return types return np.squeeze(np.var(samples, axis=axis))
def _samples_to_counts(self, samples, obs, num_wires): """Groups the samples into a dictionary showing number of occurences for each possible outcome. The format of the dictionary depends on obs.return_type, which is set when calling measurements.counts by setting the kwarg all_outcomes (bool). By default, the dictionary will only contain the observed outcomes. Optionally (all_outcomes=True) the dictionary will instead contain all possible outcomes, with a count of 0 for those not observed. See example. Args: samples: samples in an array of dimension ``(shots,len(wires))`` obs (Observable): the observable sampled num_wires (int): number of wires the sampled observable was performed on Returns: dict: dictionary with format ``{'outcome': num_occurences}``, including all outcomes for the sampled observable **Example** >>> samples tensor([[0, 0], [0, 0], [1, 0]], requires_grad=True) By default, this will return: >>> self._samples_to_counts(samples, obs, num_wires) {'00': 2, '10': 1} However, if obs.return_type is AllCounts, this will return: >>> self._samples_to_counts(samples, obs, num_wires) {'00': 2, '01': 0, '10': 1, '11': 0} The variable all_outcomes can be set when running measurements.counts, i.e.: .. code-block:: python3 dev = qml.device("default.qubit", wires=2, shots=4) @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) return qml.counts(all_outcomes=True) """ outcomes = [] if isinstance(obs, MeasurementProcess): # convert samples and outcomes (if using) from arrays to str for dict keys samples = ["".join([str(s.item()) for s in sample]) for sample in samples] if obs.return_type is AllCounts: outcomes = self.generate_basis_states(num_wires) outcomes = ["".join([str(o.item()) for o in outcome]) for outcome in outcomes] elif obs.return_type is AllCounts: outcomes = qml.eigvals(obs) # generate empty outcome dict, populate values with state counts outcome_dict = {k: np.int64(0) for k in outcomes} states, counts = np.unique(samples, return_counts=True) for s, c in zip(states, counts): outcome_dict[s] = c return outcome_dict
[docs] def sample(self, observable, shot_range=None, bin_size=None, counts=False): """Return samples of an observable. Args: observable (Observable): the observable to sample shot_range (tuple[int]): 2-tuple of integers specifying the range of samples to use. If not specified, all samples are used. bin_size (int): Divides the shot range into bins of size ``bin_size``, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin. counts (bool): whether counts (``True``) or raw samples (``False``) should be returned Raises: EigvalsUndefinedError: if no information is available about the eigenvalues of the observable Returns: Union[array[float], dict, list[dict]]: samples in an array of dimension ``(shots,)`` or counts """ # translate to wire labels used by device device_wires = self.map_wires(observable.wires) name = observable.name # Select the samples from self._samples that correspond to ``shot_range`` if provided if shot_range is None: sub_samples = self._samples else: # Indexing corresponds to: (potential broadcasting, shots, wires). Note that the last # colon (:) is required because shots is the second-to-last axis and the # Ellipsis (...) otherwise would take up broadcasting and shots axes. sub_samples = self._samples[..., slice(*shot_range), :] no_observable_provided = isinstance(observable, MeasurementProcess) if isinstance(name, str) and name in {"PauliX", "PauliY", "PauliZ", "Hadamard"}: # Process samples for observables with eigenvalues {1, -1} samples = 1 - 2 * sub_samples[..., device_wires[0]] elif no_observable_provided: # if no observable was provided then return the raw samples if len(observable.wires) != 0: # if wires are provided, then we only return samples from those wires samples = sub_samples[..., np.array(device_wires)] else: samples = sub_samples else: # Replace the basis state in the computational basis with the correct eigenvalue. # Extract only the columns of the basis samples required based on ``wires``. samples = sub_samples[..., np.array(device_wires)] # Add np.array here for Jax support. powers_of_two = 2 ** np.arange(samples.shape[-1])[::-1] indices = samples @ powers_of_two indices = np.array(indices) # Add np.array here for Jax support. try: samples = observable.eigvals()[indices] except qml.operation.EigvalsUndefinedError as e: # if observable has no info on eigenvalues, we cannot return this measurement raise qml.operation.EigvalsUndefinedError( f"Cannot compute samples of {observable.name}." ) from e num_wires = len(device_wires) if len(device_wires) > 0 else self.num_wires if bin_size is None: if counts: return self._samples_to_counts(samples, observable, num_wires) return samples if counts: shape = (-1, bin_size, num_wires) if no_observable_provided else (-1, bin_size) return [ self._samples_to_counts(bin_sample, observable, num_wires) for bin_sample in samples.reshape(shape) ] res = ( samples.reshape((num_wires, bin_size, -1)) if no_observable_provided else samples.reshape((bin_size, -1)) ) return res
[docs] def adjoint_jacobian( self, tape, starting_state=None, use_device_state=False ): # pylint: disable=too-many-statements """Implements the adjoint method outlined in `Jones and Gacon <https://arxiv.org/abs/2009.02823>`__ to differentiate an input tape. After a forward pass, the circuit is reversed by iteratively applying inverse (adjoint) gates to scan backwards through the circuit. .. note:: The adjoint differentiation method has the following restrictions: * As it requires knowledge of the statevector, only statevector simulator devices can be used. * Only expectation values are supported as measurements. * Does not work for parametrized observables like :class:`~.Hamiltonian` or :class:`~.Hermitian`. Args: tape (.QuantumTape): circuit that the function takes the gradient of Keyword Args: starting_state (tensor_like): post-forward pass state to start execution with. It should be complex-valued. Takes precedence over ``use_device_state``. use_device_state (bool): use current device state to initialize. A forward pass of the same circuit should be the last thing the device has executed. If a ``starting_state`` is provided, that takes precedence. Returns: array or tuple[array]: the derivative of the tape with respect to trainable parameters. Dimensions are ``(len(observables), len(trainable_params))``. Raises: QuantumFunctionError: if the input tape has measurements that are not expectation values or contains a multi-parameter operation aside from :class:`~.Rot` """ # broadcasted inner product not summing over first dimension of b sum_axes = tuple(range(1, self.num_wires + 1)) # pylint: disable=unnecessary-lambda-assignment dot_product_real = lambda b, k: self._real(qmlsum(self._conj(b) * k, axis=sum_axes)) for m in tape.measurements: if m.return_type is not Expectation: raise qml.QuantumFunctionError( "Adjoint differentiation method does not support" f" measurement {m.return_type.value}" ) if m.obs.name == "Hamiltonian": raise qml.QuantumFunctionError( "Adjoint differentiation method does not support Hamiltonian observables." ) if not hasattr(m.obs, "base_name"): m.obs.base_name = None # This is needed for when the observable is a tensor product if self.shot_vector is not None: raise qml.QuantumFunctionError("Adjoint does not support shot vectors.") if self.shots is not None: warnings.warn( "Requested adjoint differentiation to be computed with finite shots." " The derivative is always exact when using the adjoint differentiation method.", UserWarning, ) # Initialization of state if starting_state is not None: ket = self._reshape(starting_state, [2] * self.num_wires) else: if not use_device_state: self.reset() self.execute(tape) ket = self._pre_rotated_state n_obs = len(tape.observables) bras = np.empty([n_obs] + [2] * self.num_wires, dtype=np.complex128) for kk in range(n_obs): bras[kk, ...] = self._apply_operation(ket, tape.observables[kk]) expanded_ops = [] for op in reversed(tape.operations): if op.num_params > 1: if isinstance(op, qml.Rot) and not op.inverse: ops = op.decomposition() expanded_ops.extend(reversed(ops)) else: raise qml.QuantumFunctionError( f"The {op.name} operation is not supported using " 'the "adjoint" differentiation method' ) else: if op.name not in ("QubitStateVector", "BasisState", "Snapshot"): expanded_ops.append(op) trainable_params = [] for k in tape.trainable_params: # pylint: disable=protected-access if hasattr(tape._par_info[k]["op"], "return_type"): warnings.warn( "Differentiating with respect to the input parameters of " f"{tape._par_info[k]['op'].name} is not supported with the " "adjoint differentiation method. Gradients are computed " "only with regards to the trainable parameters of the circuit.\n\n Mark " "the parameters of the measured observables as non-trainable " "to silence this warning.", UserWarning, ) else: trainable_params.append(k) jac = np.zeros((len(tape.observables), len(trainable_params))) param_number = len(tape.get_parameters(trainable_only=False, operations_only=True)) - 1 trainable_param_number = len(trainable_params) - 1 for op in expanded_ops: adj_op = qml.adjoint(op) ket = self._apply_operation(ket, adj_op) if op.grad_method is not None: if param_number in trainable_params: d_op_matrix = operation_derivative(op) ket_temp = self._apply_unitary(ket, d_op_matrix, op.wires) jac[:, trainable_param_number] = 2 * dot_product_real(bras, ket_temp) trainable_param_number -= 1 param_number -= 1 for kk in range(n_obs): bras[kk, ...] = self._apply_operation(bras[kk, ...], adj_op) if qml.active_return(): # postprocess the jacobian for the new return_type system return self._adjoint_jacobian_processing(jac) return jac
@staticmethod def _adjoint_jacobian_processing(jac): """ Post-process the Jacobian matrix returned by ``adjoint_jacobian`` for the new return type system. """ jac = np.squeeze(jac) if jac.ndim == 0: return np.array(jac) if jac.ndim == 1: return tuple(np.array(j) for j in jac) # must be 2-dimensional return tuple(tuple(np.array(j_) for j_ in j) for j in jac)