# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2022, 2023.
#
# 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.
"""The variational quantum eigensolver algorithm."""
from __future__ import annotations
import logging
from time import time
from collections.abc import Callable
from typing import Any
import numpy as np
from qiskit.circuit import QuantumCircuit
from qiskit.primitives import BaseEstimator
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit_algorithms.gradients import BaseEstimatorGradient
from ..exceptions import AlgorithmError
from ..list_or_dict import ListOrDict
from ..optimizers import Optimizer, Minimizer, OptimizerResult
from ..variational_algorithm import VariationalAlgorithm, VariationalResult
from .minimum_eigensolver import MinimumEigensolver, MinimumEigensolverResult
from ..observables_evaluator import estimate_observables
from ..utils import validate_initial_point, validate_bounds
# private function as we expect this to be updated in the next released
from ..utils.set_batching import _set_default_batchsize
logger = logging.getLogger(__name__)
[docs]class VQE(VariationalAlgorithm, MinimumEigensolver):
r"""The Variational Quantum Eigensolver (VQE) algorithm.
VQE is a hybrid quantum-classical algorithm that uses a variational technique to find the
minimum eigenvalue of a given Hamiltonian operator :math:`H`.
The ``VQE`` algorithm is executed using an :attr:`estimator` primitive, which computes
expectation values of operators (observables).
An instance of ``VQE`` also requires an :attr:`ansatz`, a parameterized
:class:`.QuantumCircuit`, to prepare the trial state :math:`|\psi(\vec\theta)\rangle`. It also
needs a classical :attr:`optimizer` which varies the circuit parameters :math:`\vec\theta` such
that the expectation value of the operator on the corresponding state approaches a minimum,
.. math::
\min_{\vec\theta} \langle\psi(\vec\theta)|H|\psi(\vec\theta)\rangle.
The :attr:`estimator` is used to compute this expectation value for every optimization step.
The optimizer can either be one of Qiskit's optimizers, such as
:class:`~qiskit_algorithms.optimizers.SPSA` or a callable with the following signature:
.. code-block:: python
from qiskit_algorithms.optimizers import OptimizerResult
def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
# Note that the callable *must* have these argument names!
# Args:
# fun (callable): the function to minimize
# x0 (np.ndarray): the initial point for the optimization
# jac (callable, optional): the gradient of the objective function
# bounds (list, optional): a list of tuples specifying the parameter bounds
result = OptimizerResult()
result.x = # optimal parameters
result.fun = # optimal function value
return result
The above signature also allows one to use any SciPy minimizer, for instance as
.. code-block:: python
from functools import partial
from scipy.optimize import minimize
optimizer = partial(minimize, method="L-BFGS-B")
The following attributes can be set via the initializer but can also be read and updated once
the VQE object has been constructed.
Attributes:
estimator (BaseEstimator): The estimator primitive to compute the expectation value of the
Hamiltonian operator.
ansatz (QuantumCircuit): A parameterized quantum circuit to prepare the trial state.
optimizer (Optimizer | Minimizer): A classical optimizer to find the minimum energy. This
can either be a Qiskit :class:`.Optimizer` or a callable implementing the
:class:`.Minimizer` protocol.
gradient (BaseEstimatorGradient | None): An optional estimator gradient to be used with the
optimizer.
callback (Callable[[int, np.ndarray, float, dict[str, Any]], None] | None): A callback that
can access the intermediate data at each optimization step. These data are: the
evaluation count, the optimizer parameters for the ansatz, the evaluated mean, and the
metadata dictionary.
References:
[1]: Peruzzo, A., et al, "A variational eigenvalue solver on a quantum processor"
`arXiv:1304.3061 <https://arxiv.org/abs/1304.3061>`__
"""
def __init__(
self,
estimator: BaseEstimator,
ansatz: QuantumCircuit,
optimizer: Optimizer | Minimizer,
*,
gradient: BaseEstimatorGradient | None = None,
initial_point: np.ndarray | None = None,
callback: Callable[[int, np.ndarray, float, dict[str, Any]], None] | None = None,
) -> None:
r"""
Args:
estimator: The estimator primitive to compute the expectation value of the
Hamiltonian operator.
ansatz: A parameterized quantum circuit to prepare the trial state.
optimizer: A classical optimizer to find the minimum energy. This can either be a
Qiskit :class:`.Optimizer` or a callable implementing the :class:`.Minimizer`
protocol.
gradient: An optional estimator gradient to be used with the optimizer.
initial_point: An optional initial point (i.e. initial parameter values) for the
optimizer. The length of the initial point must match the number of :attr:`ansatz`
parameters. If ``None``, a random point will be generated within certain parameter
bounds. ``VQE`` will look to the ansatz for these bounds. If the ansatz does not
specify bounds, bounds of :math:`-2\pi`, :math:`2\pi` will be used.
callback: A callback that can access the intermediate data at each optimization step.
These data are: the evaluation count, the optimizer parameters for the ansatz, the
estimated value, and the metadata dictionary.
"""
super().__init__()
self.estimator = estimator
self.ansatz = ansatz
self.optimizer = optimizer
self.gradient = gradient
# this has to go via getters and setters due to the VariationalAlgorithm interface
self.initial_point = initial_point
self.callback = callback
@property
def initial_point(self) -> np.ndarray | None:
return self._initial_point
@initial_point.setter
def initial_point(self, value: np.ndarray | None) -> None:
self._initial_point = value
[docs] def compute_minimum_eigenvalue(
self,
operator: BaseOperator,
aux_operators: ListOrDict[BaseOperator] | None = None,
) -> VQEResult:
self._check_operator_ansatz(operator)
initial_point = validate_initial_point(self.initial_point, self.ansatz)
bounds = validate_bounds(self.ansatz)
start_time = time()
evaluate_energy = self._get_evaluate_energy(self.ansatz, operator)
if self.gradient is not None:
evaluate_gradient = self._get_evaluate_gradient(self.ansatz, operator)
else:
evaluate_gradient = None
# perform optimization
if callable(self.optimizer):
optimizer_result = self.optimizer(
fun=evaluate_energy, # type: ignore[arg-type]
x0=initial_point,
jac=evaluate_gradient,
bounds=bounds,
)
else:
# we always want to submit as many estimations per job as possible for minimal
# overhead on the hardware
was_updated = _set_default_batchsize(self.optimizer)
optimizer_result = self.optimizer.minimize(
fun=evaluate_energy, # type: ignore[arg-type]
x0=initial_point,
jac=evaluate_gradient, # type: ignore[arg-type]
bounds=bounds,
)
# reset to original value
if was_updated:
self.optimizer.set_max_evals_grouped(None)
optimizer_time = time() - start_time
logger.info(
"Optimization complete in %s seconds.\nFound optimal point %s",
optimizer_time,
optimizer_result.x,
)
if aux_operators is not None:
aux_operators_evaluated = estimate_observables(
self.estimator,
self.ansatz,
aux_operators,
optimizer_result.x, # type: ignore[arg-type]
)
else:
aux_operators_evaluated = None
return self._build_vqe_result(
self.ansatz,
optimizer_result,
aux_operators_evaluated, # type: ignore[arg-type]
optimizer_time,
)
[docs] @classmethod
def supports_aux_operators(cls) -> bool:
return True
def _get_evaluate_energy(
self,
ansatz: QuantumCircuit,
operator: BaseOperator,
) -> Callable[[np.ndarray], np.ndarray | float]:
"""Returns a function handle to evaluate the energy at given parameters for the ansatz.
This is the objective function to be passed to the optimizer that is used for evaluation.
Args:
ansatz: The ansatz preparing the quantum state.
operator: The operator whose energy to evaluate.
Returns:
A callable that computes and returns the energy of the hamiltonian of each parameter.
Raises:
AlgorithmError: If the primitive job to evaluate the energy fails.
"""
num_parameters = ansatz.num_parameters
# avoid creating an instance variable to remain stateless regarding results
eval_count = 0
def evaluate_energy(parameters: np.ndarray) -> np.ndarray | float:
nonlocal eval_count
# handle broadcasting: ensure parameters is of shape [array, array, ...]
parameters = np.reshape(parameters, (-1, num_parameters)).tolist()
batch_size = len(parameters)
try:
job = self.estimator.run(batch_size * [ansatz], batch_size * [operator], parameters)
estimator_result = job.result()
except Exception as exc:
raise AlgorithmError("The primitive job to evaluate the energy failed!") from exc
values = estimator_result.values
if self.callback is not None:
metadata = estimator_result.metadata
for params, value, meta in zip(parameters, values, metadata):
eval_count += 1
self.callback(eval_count, params, value, meta)
energy = values[0] if len(values) == 1 else values
return energy
return evaluate_energy
def _get_evaluate_gradient(
self,
ansatz: QuantumCircuit,
operator: BaseOperator,
) -> Callable[[np.ndarray], np.ndarray]:
"""Get a function handle to evaluate the gradient at given parameters for the ansatz.
Args:
ansatz: The ansatz preparing the quantum state.
operator: The operator whose energy to evaluate.
Returns:
A function handle to evaluate the gradient at given parameters for the ansatz.
Raises:
AlgorithmError: If the primitive job to evaluate the gradient fails.
"""
def evaluate_gradient(parameters: np.ndarray) -> np.ndarray:
# broadcasting not required for the estimator gradients
try:
job = self.gradient.run(
[ansatz], [operator], [parameters] # type: ignore[list-item]
)
gradients = job.result().gradients
except Exception as exc:
raise AlgorithmError("The primitive job to evaluate the gradient failed!") from exc
return gradients[0]
return evaluate_gradient
def _check_operator_ansatz(self, operator: BaseOperator):
"""Check that the number of qubits of operator and ansatz match and that the ansatz is
parameterized.
"""
if operator.num_qubits != self.ansatz.num_qubits:
try:
logger.info(
"Trying to resize ansatz to match operator on %s qubits.", operator.num_qubits
)
self.ansatz.num_qubits = operator.num_qubits
except AttributeError as error:
raise AlgorithmError(
"The number of qubits of the ansatz does not match the "
"operator, and the ansatz does not allow setting the "
"number of qubits using `num_qubits`."
) from error
if self.ansatz.num_parameters == 0:
raise AlgorithmError("The ansatz must be parameterized, but has no free parameters.")
def _build_vqe_result(
self,
ansatz: QuantumCircuit,
optimizer_result: OptimizerResult,
aux_operators_evaluated: ListOrDict[tuple[complex, tuple[complex, int]]],
optimizer_time: float,
) -> VQEResult:
result = VQEResult()
result.optimal_circuit = ansatz.copy()
result.eigenvalue = optimizer_result.fun
result.cost_function_evals = optimizer_result.nfev
result.optimal_point = optimizer_result.x # type: ignore[assignment]
result.optimal_parameters = dict(
zip(self.ansatz.parameters, optimizer_result.x) # type: ignore[arg-type]
)
result.optimal_value = optimizer_result.fun
result.optimizer_time = optimizer_time
result.aux_operators_evaluated = aux_operators_evaluated # type: ignore[assignment]
result.optimizer_result = optimizer_result
return result
[docs]class VQEResult(VariationalResult, MinimumEigensolverResult):
"""The Variational Quantum Eigensolver (VQE) result."""
def __init__(self) -> None:
super().__init__()
self._cost_function_evals: int | None = None
@property
def cost_function_evals(self) -> int | None:
"""The number of cost optimizer evaluations."""
return self._cost_function_evals
@cost_function_evals.setter
def cost_function_evals(self, value: int) -> None:
self._cost_function_evals = value