qiskit_machine_learning.kernels.quantum_kernel의 소스 코드

# This code is part of Qiskit.
# (C) Copyright IBM 2021, 2022.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""Quantum Kernel Algorithm"""
from __future__ import annotations

import copy
import numbers
from typing import Sequence, Mapping, List, Tuple, NoReturn

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.circuit import Parameter, ParameterVector, ParameterExpression
from qiskit.circuit.library import ZZFeatureMap
from qiskit.circuit.parameterexpression import ParameterValueType
from qiskit.providers import Backend
from qiskit.result import Result
from qiskit.utils import QuantumInstance
from qiskit.utils.deprecation import deprecate_function

from qiskit_machine_learning.deprecation import (
from .trainable_kernel import TrainableKernel
from .base_kernel import BaseKernel
from ..exceptions import QiskitMachineLearningError

[문서]class QuantumKernel(TrainableKernel, BaseKernel): r"""Pending deprecation: A dedicated trainable quantum kernel implementation that constructs and executes quantum circuits directly. The general task of machine learning is to find and study patterns in data. For many algorithms, the datapoints are better understood in a higher dimensional feature space, through the use of a kernel function: .. math:: K(x, y) = \langle f(x), f(y)\rangle. Here K is the kernel function, x, y are n dimensional inputs. f is a map from n-dimension to m-dimension space. :math:`\langle x, y \rangle` denotes the dot product. Usually m is much larger than n. The quantum kernel algorithm calculates a kernel matrix, given datapoints x and y and feature map f, all of n dimension. This kernel matrix can then be used in classical machine learning algorithms such as support vector classification, spectral clustering or ridge regression. """ @deprecate_arguments("0.5.0", {"user_parameters": "training_parameters"}) @deprecate_function( "The QuantumKernel class has been superseded by the " "qiskit_machine_learning.kernels.FidelityQuantumKernel and " "qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel classes. " "This class will be deprecated in a future release and subsequently " "removed after that.", stacklevel=3, category=PendingDeprecationWarning, ) def __init__( self, feature_map: QuantumCircuit | None = None, enforce_psd: bool = True, batch_size: int = 900, quantum_instance: QuantumInstance | Backend | None = None, training_parameters: ParameterVector | Sequence[Parameter] | None = None, evaluate_duplicates: str = "off_diagonal", ) -> None: """ Args: feature_map: Parameterized circuit to be used as the feature map. If None is given, the `ZZFeatureMap` is used with two qubits. enforce_psd: Project to closest positive semidefinite matrix if x = y. Only enforced when not using the state vector simulator. Default True. batch_size: Number of circuits to batch together for computation. Default 900. quantum_instance: Quantum Instance or Backend training_parameters: Iterable containing ``Parameter`` objects which correspond to quantum gates on the feature map circuit which may be tuned. If users intend to tune feature map parameters to find optimal values, this field should be set. evaluate_duplicates: Defines a strategy how kernel matrix elements are evaluated if duplicate samples are found. Possible values are: - ``all`` means that all kernel matrix elements are evaluated, even the diagonal ones when training. This may introduce additional noise in the matrix. - ``off_diagonal`` when training the matrix diagonal is set to `1`, the rest elements are fully evaluated, e.g., for two identical samples in the dataset. When inferring, all elements are evaluated. This is the default value. - ``none`` when training the diagonal is set to `1` and if two identical samples are found in the dataset the corresponding matrix element is set to `1`. When inferring, matrix elements for identical samples are set to `1`. Raises: ValueError: When unsupported value is passed to `evaluate_duplicates`. """ super().__init__(feature_map=feature_map, enforce_psd=enforce_psd) # Class fields self._feature_map: QuantumCircuit | None = None # type is required by mypy self._unbound_feature_map = None self._training_parameters = None self._training_parameter_binds = None self._enforce_psd = enforce_psd self._batch_size = batch_size # convert to QuantumInstance if an instance of Backend is passed self.quantum_instance = quantum_instance eval_duplicates = evaluate_duplicates.lower() if eval_duplicates not in ("all", "off_diagonal", "none"): raise ValueError( f"Unsupported value passed as evaluate_duplicates: {evaluate_duplicates}" ) self._evaluate_duplicates = eval_duplicates # Setters self.feature_map = feature_map if feature_map is not None else ZZFeatureMap(2) if training_parameters is not None: self.training_parameters = training_parameters @property def feature_map(self) -> QuantumCircuit: """Return feature map""" return self._feature_map @feature_map.setter def feature_map(self, feature_map: QuantumCircuit) -> None: """ Set feature map. The ``unbound_feature_map`` field will be automatically updated when this field is set, and ``training_parameters`` and ``training_parameter_binds`` fields will be reset to ``None``. """ self._feature_map = feature_map self._unbound_feature_map = copy.deepcopy(self._feature_map) self._training_parameters = None self._training_parameter_binds = None @property def unbound_feature_map(self) -> QuantumCircuit: """Return unbound feature map""" return copy.deepcopy(self._unbound_feature_map) @property def quantum_instance(self) -> QuantumInstance: """Return quantum instance""" return self._quantum_instance @quantum_instance.setter def quantum_instance(self, quantum_instance: Backend | QuantumInstance) -> None: """Set quantum instance""" if isinstance(quantum_instance, Backend): quantum_instance = QuantumInstance(quantum_instance) self._quantum_instance = quantum_instance @property def training_parameters(self) -> ParameterVector | Sequence[Parameter] | None: """Return the vector of training parameters.""" return copy.copy(self._training_parameters) @training_parameters.setter def training_parameters(self, training_params: ParameterVector | Sequence[Parameter]) -> None: """Set the training parameters""" self._training_parameter_binds = { training_param: training_param for training_param in training_params } self._training_parameters = copy.deepcopy(training_params)
[문서] def assign_training_parameters( self, parameter_values: Mapping[Parameter, ParameterValueType] | Sequence[ParameterValueType], ) -> None: """ Assign training parameters in the quantum kernel's feature map. Args: parameter_values (dict or iterable): Either a dictionary or iterable specifying the new parameter values. If a dict, it specifies the mapping from ``current_parameter`` to ``new_parameter``, where ``new_parameter`` can be a parameter expression or a numeric value. If an iterable, the elements are assigned to the existing parameters in the order of ``QuantumKernel.training_parameters``. Raises: ValueError: Incompatible number of training parameters and values """ values = parameter_values if self._training_parameters is None: raise ValueError( f""" The number of parameter values ({len(values)}) does not match the number of training parameters tracked by the quantum kernel (None). """ ) # Get the input parameters. These should remain unaffected by assigning of training parameters. input_params = list( set(self._unbound_feature_map.parameters) - set(self._training_parameters) ) # If iterable of values is passed, the length must match length of training_parameters field if isinstance(values, (Sequence, np.ndarray)): if len(values) != len(self._training_parameters): raise ValueError( f""" The number of parameter values ({len(values)}) does not match the number of training parameters tracked by the quantum kernel ({len(self._training_parameters)}). """ ) values = {p: values[i] for i, p in enumerate(self._training_parameters)} else: if not isinstance(values, dict): raise ValueError( f""" 'values' must be of type Dict or Sequence. Type {type(values)} is not supported. """ ) # All input keys must exist in the circuit # This check actually catches some well defined assignments; # however; we throw an error to be consistent with the behavior # of QuantumCircuit's parameter binding. unknown_parameters = list(set(values.keys()) - set(self._training_parameters)) if len(unknown_parameters) > 0: raise ValueError( f"Cannot bind parameters ({unknown_parameters}) not tracked by the quantum kernel." ) # Because QuantumKernel supports parameter rebinding, entries of the `values` dictionary must # be handled differently depending on whether they represent numerical assignments or parameter # reassignments. However, re-ordering the values dictionary inherently changes the expected # behavior of parameter binding, as entries in the values dict do not commute with one another # in general. To resolve this issue, we handle each entry of the values dict one at a time. for param, bind in values.items(): if isinstance(bind, ParameterExpression): self._unbound_feature_map.assign_parameters({param: bind}, inplace=True) # Training params are all non-input params in the unbound feature map # This list comprehension ensures that self._training_parameters is ordered # in a way that is consistent with self.feature_map.parameters self._training_parameters = [ p for p in self._unbound_feature_map.parameters if (p not in input_params) ] # Remove param if it was overwritten if param not in self._training_parameters: del self._training_parameter_binds[param] # Add new parameters for sub_param in bind.parameters: if sub_param not in self._training_parameter_binds.keys(): self._training_parameter_binds[sub_param] = sub_param # If parameter is being set to expression of itself, training_parameter_binds # reflects a self-bind if param in bind.parameters: self._training_parameter_binds[param] = param # If assignment is numerical, update the param_binds elif isinstance(bind, numbers.Number): self._training_parameter_binds[param] = bind else: raise ValueError( f""" Parameters can only be bound to numeric values, Parameters, or ParameterExpressions. Type {type(bind)} is not supported. """ ) # Reorder dict according to self._training_parameters self._training_parameter_binds = { param: self._training_parameter_binds[param] for param in self._training_parameters } # Update feature map with numerical parameter assignments self._feature_map = self._unbound_feature_map.assign_parameters( self._training_parameter_binds )
@property def training_parameter_binds(self) -> Mapping[Parameter, float] | None: """Return a copy of the current training parameter mappings for the feature map circuit.""" return copy.deepcopy(self._training_parameter_binds)
[문서] def bind_training_parameters( self, values: Mapping[Parameter, ParameterValueType] | Sequence[ParameterValueType] ) -> None: """ Alternate function signature for ``assign_training_parameters`` """ self.assign_training_parameters(values)
[문서] def get_unbound_training_parameters(self) -> List[Parameter]: """Return a list of any unbound training parameters in the feature map circuit.""" unbound_training_params = [] if self._training_parameter_binds is not None: # Get all training parameters not associated with numerical values unbound_training_params = [ val for val in self._training_parameter_binds.values() if not isinstance(val, numbers.Number) ] return unbound_training_params
@property @deprecate_property("0.5.0", new_name="training_parameters") def user_parameters(self) -> ParameterVector | Sequence[Parameter] | None: """[Deprecated property]Return the vector of training parameters.""" return self.training_parameters @user_parameters.setter @deprecate_property("0.5.0", new_name="training_parameters") def user_parameters(self, training_params: ParameterVector | Sequence[Parameter]) -> None: """[Deprecated property setter]Set the training parameters""" self.training_parameters = training_params
[문서] @deprecate_method("0.5.0", new_name="assign_training_parameters") def assign_user_parameters( self, values: Mapping[Parameter, ParameterValueType] | Sequence[ParameterValueType] ) -> None: """ [Deprecated method]Assign training parameters in the ``QuantumKernel`` feature map. Otherwise, just like ``assign_training_parameters``. """ self.assign_training_parameters(values)
@property @deprecate_property("0.5.0", new_name="training_parameter_binds") def user_param_binds(self) -> Mapping[Parameter, float] | None: """ [Deprecated property]Return a copy of the current training parameter mappings for the feature map circuit. """ return self.training_parameter_binds
[문서] @deprecate_method("0.5.0", new_name="bind_training_parameters") def bind_user_parameters( self, values: Mapping[Parameter, ParameterValueType] | Sequence[ParameterValueType] ) -> None: """ [Deprecated method]Alternate function signature for ``assign_training_parameters`` """ self.bind_training_parameters(values)
[문서] @deprecate_method("0.5.0", new_name="get_unbound_training_parameters") def get_unbound_user_parameters(self) -> List[Parameter]: """ [Deprecated method]Return a list of any unbound training parameters in the feature map circuit. """ return self.get_unbound_training_parameters()
[문서] def construct_circuit( self, x: ParameterVector, y: ParameterVector = None, measurement: bool = True, is_statevector_sim: bool = False, ) -> QuantumCircuit: r""" Construct inner product circuit for given datapoints and feature map. If using `statevector_simulator`, only construct circuit for :math:`\Psi(x)|0\rangle`, otherwise construct :math:`Psi^dagger(y) x Psi(x)|0>` If y is None and not using `statevector_simulator`, self inner product is calculated. Args: x: first data point parameter vector y: second data point parameter vector, ignored if using statevector simulator measurement: include measurement if not using statevector simulator is_statevector_sim: use state vector simulator Returns: QuantumCircuit Raises: ValueError: - x and/or y have incompatible dimension with feature map - unbound training parameters in the feature map circuit """ qc = self._construct_circuit_with_feature_map(x, y) if y is None: y = x if not is_statevector_sim: y_dict = dict(zip(self._feature_map.parameters, y)) psi_y_dag = self._feature_map.assign_parameters(y_dict) qc.append(psi_y_dag.to_instruction().inverse(), qc.qubits) if measurement: qc.barrier(qc.qregs[0]) qc.measure(qc.qregs[0], qc.cregs[0]) return qc
def _construct_circuit_with_feature_map( self, x: ParameterVector, y: ParameterVector = None ) -> QuantumCircuit: self._check_training_parameters_bound() if len(x) != self._feature_map.num_parameters: self._raise_incompatible_feature_map("x", len(x)) if y is not None and len(y) != self._feature_map.num_parameters: self._raise_incompatible_feature_map("y", len(y)) q = QuantumRegister(self._feature_map.num_qubits, "q") c = ClassicalRegister(self._feature_map.num_qubits, "c") qc = QuantumCircuit(q, c) x_dict = dict(zip(self._feature_map.parameters, x)) psi_x = self._feature_map.assign_parameters(x_dict) qc.append(psi_x.to_instruction(), qc.qubits) return qc # just a synonym for consistency with other "*_statevector()" methods def _construct_circuit_statevector( self, x: ParameterVector, y: ParameterVector = None ) -> QuantumCircuit: """This is just a synonym for ``_construct_circuit_with_feature_map``""" return self._construct_circuit_with_feature_map(x, y) def _compute_overlap_statevector( self, idx1: int, idx2: int, results: List[np.ndarray] ) -> float: """ Helper function to compute overlap for given input if a statevector simulator is used. """ # |<0|Psi^dagger(y) x Psi(x)|0>|^2, take the amplitude v_a, v_b = results[idx1], results[idx2] tmp = np.vdot(v_a, v_b) kernel_value = np.vdot(tmp, tmp).real # pylint: disable=no-member return kernel_value def _compute_overlap(self, idx: int, results: Result) -> float: """ Helper function to compute overlap for given input when a non-statevector simulator or device is used. """ measurement_basis = "0" * self._feature_map.num_qubits result = results.get_counts(idx) kernel_value = result.get(measurement_basis, 0) / sum(result.values()) return kernel_value
[문서] def evaluate(self, x_vec: np.ndarray, y_vec: np.ndarray = None) -> np.ndarray: r""" Construct kernel matrix for given data and feature map If y_vec is None, self inner product is calculated. If using `statevector_simulator`, only build circuits for :math:`\Psi(x)|0\rangle`, then perform inner product classically. Args: x_vec: 1D or 2D array of datapoints, NxD, where N is the number of datapoints, D is the feature dimension y_vec: 1D or 2D array of datapoints, MxD, where M is the number of datapoints, D is the feature dimension Returns: 2D matrix, NxM Raises: QiskitMachineLearningError: - A quantum instance or backend has not been provided ValueError: - unbound training parameters in the feature map circuit - x_vec and/or y_vec are not one or two dimensional arrays - x_vec and y_vec have have incompatible dimensions - x_vec and/or y_vec have incompatible dimension with feature map and and feature map can not be modified to match. """ self._check_training_parameters_bound() if self._quantum_instance is None: raise QiskitMachineLearningError( "A QuantumInstance or Backend must be supplied to evaluate a quantum kernel." ) x_vec, y_vec = self._validate_input(x_vec, y_vec) # determine if calculating self inner product is_symmetric = True if y_vec is None: y_vec = x_vec elif not np.array_equal(x_vec, y_vec): is_symmetric = False # initialize kernel matrix kernel = np.zeros((x_vec.shape[0], y_vec.shape[0])) if is_symmetric and self._evaluate_duplicates != "all": np.fill_diagonal(kernel, 1) # calculate kernel if self._quantum_instance.is_statevector: kernel = self._calculate_kernel_statevector(x_vec, y_vec, is_symmetric, kernel) else: kernel = self._calculate_kernel(x_vec, y_vec, is_symmetric, kernel) return kernel
def _validate_input( self, x_vec: np.ndarray, y_vec: np.ndarray ) -> Tuple[np.ndarray, np.ndarray]: x_vec = np.asarray(x_vec) if x_vec.ndim > 2: raise ValueError("x_vec must be a 1D or 2D array") if x_vec.ndim == 1: x_vec = np.reshape(x_vec, (-1, len(x_vec))) if x_vec.shape[1] != self._feature_map.num_parameters: # before raising an error we try to adjust the feature map # to the required number of qubit. try: self._feature_map.num_qubits = x_vec.shape[1] except AttributeError: self._raise_incompatible_feature_map("x_vec", x_vec.shape[1]) if y_vec is not None: y_vec = np.asarray(y_vec) if y_vec.ndim == 1: y_vec = np.reshape(y_vec, (-1, len(y_vec))) if y_vec.ndim > 2: raise ValueError("y_vec must be a 1D or 2D array") if y_vec.shape[1] != x_vec.shape[1]: raise ValueError( "x_vec and y_vec have incompatible dimensions.\n" f"x_vec has {x_vec.shape[1]} dimensions, but y_vec has {y_vec.shape[1]}." ) return x_vec, y_vec def _calculate_kernel_statevector( self, x_vec: np.ndarray, y_vec: np.ndarray, is_symmetric: bool, kernel: np.ndarray ) -> np.ndarray: # get indices to calculate row_indices, col_indices = self._get_indices(x_vec, y_vec, is_symmetric) if is_symmetric: to_be_computed_data = x_vec else: # not symmetric to_be_computed_data = np.concatenate((x_vec, y_vec)) feature_map_params = ParameterVector("par_x", self._feature_map.num_parameters) parameterized_circuit = self._construct_circuit_statevector( feature_map_params, feature_map_params, ) parameterized_circuit = self._quantum_instance.transpile( parameterized_circuit, pass_manager=self._quantum_instance.unbound_pass_manager )[0] statevectors = [] for min_idx in range(0, len(to_be_computed_data), self._batch_size): max_idx = min(min_idx + self._batch_size, len(to_be_computed_data)) circuits = [ parameterized_circuit.assign_parameters({feature_map_params: x}) for x in to_be_computed_data[min_idx:max_idx] ] if self._quantum_instance.bound_pass_manager is not None: circuits = self._quantum_instance.transpile( circuits, pass_manager=self._quantum_instance.bound_pass_manager ) results = self._quantum_instance.execute(circuits, had_transpiled=True) for j in range(max_idx - min_idx): statevectors.append(results.get_statevector(j)) offset = 0 if is_symmetric else len(x_vec) for (i, j) in zip(row_indices, col_indices): x_i = x_vec[i] y_j = y_vec[j] # fill in ones for identical samples if np.all(x_i == y_j) and self._evaluate_duplicates == "none": kernel_value = 1.0 else: kernel_value = self._compute_overlap_statevector(i, j + offset, statevectors) kernel[i, j] = kernel_value if is_symmetric: kernel[j, i] = kernel_value return kernel def _calculate_kernel( self, x_vec: np.ndarray, y_vec: np.ndarray, is_symmetric: bool, kernel: np.ndarray ) -> np.ndarray: # get indices to calculate row_indices, col_indices = self._get_indices(x_vec, y_vec, is_symmetric) feature_map_params_x = ParameterVector("par_x", self._feature_map.num_parameters) feature_map_params_y = ParameterVector("par_y", self._feature_map.num_parameters) parameterized_circuit = self.construct_circuit(feature_map_params_x, feature_map_params_y) parameterized_circuit = self._quantum_instance.transpile( parameterized_circuit, pass_manager=self._quantum_instance.unbound_pass_manager )[0] for idx in range(0, len(row_indices), self._batch_size): to_be_computed_data_pair = [] to_be_computed_index = [] for sub_idx in range(idx, min(idx + self._batch_size, len(row_indices))): i = row_indices[sub_idx] j = col_indices[sub_idx] x_i = x_vec[i] y_j = y_vec[j] # fill in ones for identical samples if np.all(x_i == y_j) and self._evaluate_duplicates == "none": kernel[i, j] = 1 if is_symmetric: kernel[j, i] = 1 else: # otherwise evaluate the element to_be_computed_data_pair.append((x_i, y_j)) to_be_computed_index.append((i, j)) circuits = [ parameterized_circuit.assign_parameters( {feature_map_params_x: x, feature_map_params_y: y} ) for x, y in to_be_computed_data_pair ] if self._quantum_instance.bound_pass_manager is not None: circuits = self._quantum_instance.transpile( circuits, pass_manager=self._quantum_instance.bound_pass_manager ) results = self._quantum_instance.execute(circuits, had_transpiled=True) matrix_elements = [ self._compute_overlap(circuit, results) for circuit in range(len(circuits)) ] for (i, j), value in zip(to_be_computed_index, matrix_elements): kernel[i, j] = value if is_symmetric: kernel[j, i] = kernel[i, j] if self._enforce_psd and is_symmetric: # Find the closest positive semi-definite approximation to symmetric kernel matrix. # The (symmetric) matrix should always be positive semi-definite by construction, # but this can be violated in case of noise, such as sampling noise, thus the # adjustment is only done if NOT using the statevector simulation. D, U = np.linalg.eig(kernel) # pylint: disable=invalid-name kernel = U @ np.diag(np.maximum(0, D)) @ U.transpose() return kernel def _get_indices( self, x_vec: np.ndarray, y_vec: np.ndarray, is_symmetric: bool ) -> Tuple[np.ndarray, np.ndarray]: # get indices to calculate if is_symmetric: if self._evaluate_duplicates == "all": row_indices, col_indices = np.triu_indices(x_vec.shape[0]) else: # exclude diagonal row_indices, col_indices = np.triu_indices(x_vec.shape[0], k=1) else: row_indices, col_indices = np.indices((x_vec.shape[0], y_vec.shape[0])) row_indices = np.asarray(row_indices.flat) col_indices = np.asarray(col_indices.flat) return row_indices, col_indices def _check_training_parameters_bound(self) -> None: # Ensure all training parameters have been bound in the feature map circuit. unbound_params = self.get_unbound_training_parameters() if unbound_params: raise ValueError( f""" The feature map circuit contains unbound training parameters ({unbound_params}). All training parameters must be bound to numerical values before evaluating the kernel matrix. """ ) def _raise_incompatible_feature_map(self, vec_name: str, vec_len: int) -> NoReturn: raise ValueError( f"{vec_name} and class feature map have incompatible dimensions.\n" f"{vec_name} has {vec_len} dimensions, " f"but feature map has {self._feature_map.num_parameters}." )