# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# 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.
"""Optimizer interface"""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Callable
from enum import IntEnum
import logging
from typing import Any, Union, Protocol
import numpy as np
import scipy
from qiskit.algorithms.algorithm_result import AlgorithmResult
logger = logging.getLogger(__name__)
POINT = Union[float, np.ndarray]
[Doku]class OptimizerResult(AlgorithmResult):
"""The result of an optimization routine."""
def __init__(self) -> None:
super().__init__()
self._x: POINT | None = None
self._fun: float | None = None
self._jac: POINT | None = None
self._nfev: int | None = None
self._njev: int | None = None
self._nit: int | None = None
@property
def x(self) -> POINT | None:
"""The final point of the minimization."""
return self._x
@x.setter
def x(self, x: POINT | None) -> None:
"""Set the final point of the minimization."""
self._x = x
@property
def fun(self) -> float | None:
"""The final value of the minimization."""
return self._fun
@fun.setter
def fun(self, fun: float | None) -> None:
"""Set the final value of the minimization."""
self._fun = fun
@property
def jac(self) -> POINT | None:
"""The final gradient of the minimization."""
return self._jac
@jac.setter
def jac(self, jac: POINT | None) -> None:
"""Set the final gradient of the minimization."""
self._jac = jac
@property
def nfev(self) -> int | None:
"""The total number of function evaluations."""
return self._nfev
@nfev.setter
def nfev(self, nfev: int | None) -> None:
"""Set the total number of function evaluations."""
self._nfev = nfev
@property
def njev(self) -> int | None:
"""The total number of gradient evaluations."""
return self._njev
@njev.setter
def njev(self, njev: int | None) -> None:
"""Set the total number of gradient evaluations."""
self._njev = njev
@property
def nit(self) -> int | None:
"""The total number of iterations."""
return self._nit
@nit.setter
def nit(self, nit: int | None) -> None:
"""Set the total number of iterations."""
self._nit = nit
[Doku]class Minimizer(Protocol):
"""Callable Protocol for minimizer.
This interface is based on `SciPy's optimize module
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html>`__.
This protocol defines a callable taking the following parameters:
fun
The objective function to minimize (for example the energy in the case of the VQE).
x0
The initial point for the optimization.
jac
The gradient of the objective function.
bounds
Parameters bounds for the optimization. Note that these might not be supported
by all optimizers.
and which returns a minimization result object (either SciPy's or Qiskit's).
"""
# pylint: disable=invalid-name
def __call__(
self,
fun: Callable[[np.ndarray], float],
x0: np.ndarray,
jac: Callable[[np.ndarray], np.ndarray] | None,
bounds: list[tuple[float, float]] | None,
) -> scipy.optimize.OptimizeResult | OptimizerResult:
"""Minimize the objective function.
This interface is based on `SciPy's optimize module <https://docs.scipy.org/doc
/scipy/reference/generated/scipy.optimize.minimize.html>`__.
Args:
fun: The objective function to minimize (for example the energy in the case of the VQE).
x0: The initial point for the optimization.
jac: The gradient of the objective function.
bounds: Parameters bounds for the optimization. Note that these might not be supported
by all optimizers.
Returns:
The minimization result object (either SciPy's or Qiskit's).
"""
...
[Doku]class OptimizerSupportLevel(IntEnum):
"""Support Level enum for features such as bounds, gradient and initial point"""
# pylint: disable=invalid-name
not_supported = 0 # Does not support the corresponding parameter in optimize()
ignored = 1 # Feature can be passed as non None but will be ignored
supported = 2 # Feature is supported
required = 3 # Feature is required and must be given, None is invalid
[Doku]class Optimizer(ABC):
"""Base class for optimization algorithm."""
@abstractmethod
def __init__(self):
"""
Initialize the optimization algorithm, setting the support
level for _gradient_support_level, _bound_support_level,
_initial_point_support_level, and empty options.
"""
self._gradient_support_level = self.get_support_level()["gradient"]
self._bounds_support_level = self.get_support_level()["bounds"]
self._initial_point_support_level = self.get_support_level()["initial_point"]
self._options = {}
self._max_evals_grouped = None
[Doku] @abstractmethod
def get_support_level(self):
"""Return support level dictionary"""
raise NotImplementedError
[Doku] def set_options(self, **kwargs):
"""
Sets or updates values in the options dictionary.
The options dictionary may be used internally by a given optimizer to
pass additional optional values for the underlying optimizer/optimization
function used. The options dictionary may be initially populated with
a set of key/values when the given optimizer is constructed.
Args:
kwargs (dict): options, given as name=value.
"""
for name, value in kwargs.items():
self._options[name] = value
logger.debug("options: %s", self._options)
# pylint: disable=invalid-name
[Doku] @staticmethod
def gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None):
"""
We compute the gradient with the numeric differentiation in the parallel way,
around the point x_center.
Args:
x_center (ndarray): point around which we compute the gradient
f (func): the function of which the gradient is to be computed.
epsilon (float): the epsilon used in the numeric differentiation.
max_evals_grouped (int): max evals grouped, defaults to 1 (i.e. no batching).
Returns:
grad: the gradient computed
"""
if max_evals_grouped is None: # no batching by default
max_evals_grouped = 1
forig = f(*((x_center,)))
grad = []
ei = np.zeros((len(x_center),), float)
todos = []
for k in range(len(x_center)):
ei[k] = 1.0
d = epsilon * ei
todos.append(x_center + d)
ei[k] = 0.0
counter = 0
chunk = []
chunks = []
length = len(todos)
# split all points to chunks, where each chunk has batch_size points
for i in range(length):
x = todos[i]
chunk.append(x)
counter += 1
# the last one does not have to reach batch_size
if counter == max_evals_grouped or i == length - 1:
chunks.append(chunk)
chunk = []
counter = 0
for chunk in chunks: # eval the chunks in order
parallel_parameters = np.concatenate(chunk)
todos_results = f(parallel_parameters) # eval the points in a chunk (order preserved)
if isinstance(todos_results, float):
grad.append((todos_results - forig) / epsilon)
else:
for todor in todos_results:
grad.append((todor - forig) / epsilon)
return np.array(grad)
[Doku] @staticmethod
def wrap_function(function, args):
"""
Wrap the function to implicitly inject the args at the call of the function.
Args:
function (func): the target function
args (tuple): the args to be injected
Returns:
function_wrapper: wrapper
"""
def function_wrapper(*wrapper_args):
return function(*(wrapper_args + args))
return function_wrapper
@property
def setting(self):
"""Return setting"""
ret = f"Optimizer: {self.__class__.__name__}\n"
params = ""
for key, value in self.__dict__.items():
if key[0] == "_":
params += f"-- {key[1:]}: {value}\n"
ret += f"{params}"
return ret
@property
def settings(self) -> dict[str, Any]:
"""The optimizer settings in a dictionary format.
The settings can for instance be used for JSON-serialization (if all settings are
serializable, which e.g. doesn't hold per default for callables), such that the
optimizer object can be reconstructed as
.. code-block::
settings = optimizer.settings
# JSON serialize and send to another server
optimizer = OptimizerClass(**settings)
"""
raise NotImplementedError("The settings method is not implemented per default.")
[Doku] @abstractmethod
def minimize(
self,
fun: Callable[[POINT], float],
x0: POINT,
jac: Callable[[POINT], POINT] | None = None,
bounds: list[tuple[float, float]] | None = None,
) -> OptimizerResult:
"""Minimize the scalar function.
Args:
fun: The scalar function to minimize.
x0: The initial point for the minimization.
jac: The gradient of the scalar function ``fun``.
bounds: Bounds for the variables of ``fun``. This argument might be ignored if the
optimizer does not support bounds.
Returns:
The result of the optimization, containing e.g. the result as attribute ``x``.
"""
raise NotImplementedError()
@property
def gradient_support_level(self):
"""Returns gradient support level"""
return self._gradient_support_level
@property
def is_gradient_ignored(self):
"""Returns is gradient ignored"""
return self._gradient_support_level == OptimizerSupportLevel.ignored
@property
def is_gradient_supported(self):
"""Returns is gradient supported"""
return self._gradient_support_level != OptimizerSupportLevel.not_supported
@property
def is_gradient_required(self):
"""Returns is gradient required"""
return self._gradient_support_level == OptimizerSupportLevel.required
@property
def bounds_support_level(self):
"""Returns bounds support level"""
return self._bounds_support_level
@property
def is_bounds_ignored(self):
"""Returns is bounds ignored"""
return self._bounds_support_level == OptimizerSupportLevel.ignored
@property
def is_bounds_supported(self):
"""Returns is bounds supported"""
return self._bounds_support_level != OptimizerSupportLevel.not_supported
@property
def is_bounds_required(self):
"""Returns is bounds required"""
return self._bounds_support_level == OptimizerSupportLevel.required
@property
def initial_point_support_level(self):
"""Returns initial point support level"""
return self._initial_point_support_level
@property
def is_initial_point_ignored(self):
"""Returns is initial point ignored"""
return self._initial_point_support_level == OptimizerSupportLevel.ignored
@property
def is_initial_point_supported(self):
"""Returns is initial point supported"""
return self._initial_point_support_level != OptimizerSupportLevel.not_supported
@property
def is_initial_point_required(self):
"""Returns is initial point required"""
return self._initial_point_support_level == OptimizerSupportLevel.required
[Doku] def print_options(self):
"""Print algorithm-specific options."""
for name in sorted(self._options):
logger.debug("%s = %s", name, str(self._options[name]))
[Doku] def set_max_evals_grouped(self, limit):
"""Set max evals grouped"""
self._max_evals_grouped = limit