250041941

Build # 250041941

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<a href="https://github.com/Qiskit/qiskit-aqua/commit/<a class=hub.com/Qiskit/qiskit-aqua/commit/90a16c24827af2c51b5cc2cd918fca93962e7de2">90a16c248<a href="https://github.com/Qiskit/qiskit-aqua/commit/90a16c24827af2c51b5cc2cd918fca93962e7de2">">Merge </a><a class="double-link" href="https://github.com/Qiskit/qiskit-aqua/commit/<a class="double-link" href="https://github.com/Qiskit/qiskit-aqua/commit/03107db3d839647fb59403b5f2dcb1eead9c4f46">03107db3d</a>">03107db3d</a><a href="https://github.com/Qiskit/qiskit-aqua/commit/90a16c24827af2c51b5cc2cd918fca93962e7de2"> into 99394e542">99394e542</a>

Pull Request Pull Request #1240: Optimizers for noisy quantum devices (scikit-quant)

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/qiskit/aqua/components/optimizers/snobfit.py

# This code is part of Qiskit.
#
# (C) Copyright IBM 2019, 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.

"""Stable Noisy Optimization by Branch and FIT algorithm (SNOBFIT) optimizer."""

import logging
import numpy as np
import skquant.opt as skq
from SQSnobFit import optset
from .optimizer import Optimizer, OptimizerSupportLevel

logger = logging.getLogger(__name__)


class SNOBFIT(Optimizer):
    """Stable Noisy Optimization by Branch and FIT algorithm.

    SnobFit is used for the optimization of derivative-free, noisy objective functions providing
    robust and fast solutions of problems with continuous variables varying within bound.

    Uses skquant.opt installed with pip install scikit-quant.
    For further detail, please refer to
    https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.
    """

    # pylint: disable=unused-argument
    def __init__(self,
                 maxiter: int = 1000,
                 maxfail: int = 10,
                 maxmp: int = None,
                 verbose: bool = False,
                 ) -> None:
        """
        Args:
            maxiter: Maximum number of function evaluations.
            maxmp: Maximum number of  model points requested for the local fit.
                 Default = 2 * number of parameters + 6 set to this value when None.
            maxfail: Maximum number of failures to improve the solution. Stops the algorithm
                    after maxfail is reached.
            verbose: Provide verbose (debugging) output.
        """
        super().__init__()
        self._maxiter = maxiter
        self._maxfail = maxfail
        self._maxmp = maxmp
        self._verbose = verbose

    def get_support_level(self):
        """ Returns support level dictionary. """
        return {
            'gradient': OptimizerSupportLevel.ignored,
            'bounds': OptimizerSupportLevel.required,
            'initial_point': OptimizerSupportLevel.required
        }

    def optimize(self, num_vars, objective_function, gradient_function=None,
                 variable_bounds=None, initial_point=None):
        """ Runs the optimization. """
        super().optimize(num_vars, objective_function, gradient_function,
                         variable_bounds, initial_point)
        snobfit_settings = {
            'maxmp': self._maxmp,
            'maxfail': self._maxfail,
            'verbose': self._verbose,
        }
        options = optset(optin=snobfit_settings)
        # counters the error when initial point is outside the acceptable bounds
        for idx, theta in enumerate(initial_point):
            if abs(theta) > variable_bounds[idx][0]:
                initial_point[idx] = initial_point[idx] % variable_bounds[idx][0]
            elif abs(theta) > variable_bounds[idx][1]:
                initial_point[idx] = initial_point[idx] % variable_bounds[idx][1]
        res, history = skq.minimize(objective_function, np.array(initial_point, dtype=float),
                                    bounds=variable_bounds, budget=self._maxiter,
                                    method="snobfit", options=options)
        return res.optpar, res.optval, len(history)

1	# This code is part of Qiskit.
2	#
3	# (C) Copyright IBM 2019, 2020.
4	#
5	# This code is licensed under the Apache License, Version 2.0. You may
6	# obtain a copy of this license in the LICENSE.txt file in the root directory
7	# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
8	#
9	# Any modifications or derivative works of this code must retain this
10	# copyright notice, and modified files need to carry a notice indicating
11	# that they have been altered from the originals.
12
13	"""Stable Noisy Optimization by Branch and FIT algorithm (SNOBFIT) optimizer."""	1✔
14
15	import logging	1✔
16	import numpy as np	1✔
17	import skquant.opt as skq	1✔
18	from SQSnobFit import optset	1✔
19	from .optimizer import Optimizer, OptimizerSupportLevel	1✔
20
21	logger = logging.getLogger(__name__)	1✔
22
23
24	class SNOBFIT(Optimizer):	1✔
25	"""Stable Noisy Optimization by Branch and FIT algorithm.
26
27	SnobFit is used for the optimization of derivative-free, noisy objective functions providing
28	robust and fast solutions of problems with continuous variables varying within bound.
29
30	Uses skquant.opt installed with pip install scikit-quant.
31	For further detail, please refer to
32	https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.
33	"""
34
35	# pylint: disable=unused-argument
36	def __init__(self,	1✔
37	maxiter: int = 1000,
38	maxfail: int = 10,
39	maxmp: int = None,
40	verbose: bool = False,
41	) -> None:
42	"""
43	Args:
44	maxiter: Maximum number of function evaluations.
45	maxmp: Maximum number of model points requested for the local fit.
46	Default = 2 * number of parameters + 6 set to this value when None.
47	maxfail: Maximum number of failures to improve the solution. Stops the algorithm
48	after maxfail is reached.
49	verbose: Provide verbose (debugging) output.
50	"""
51	super().__init__()	1✔
52	self._maxiter = maxiter	1✔
53	self._maxfail = maxfail	1✔
54	self._maxmp = maxmp	1✔
55	self._verbose = verbose	1✔
56
57	def get_support_level(self):	1✔
58	""" Returns support level dictionary. """
59	return {	1✔
60	'gradient': OptimizerSupportLevel.ignored,
61	'bounds': OptimizerSupportLevel.required,
62	'initial_point': OptimizerSupportLevel.required
63	}
64
65	def optimize(self, num_vars, objective_function, gradient_function=None,	1✔
66	variable_bounds=None, initial_point=None):
67	""" Runs the optimization. """
68	super().optimize(num_vars, objective_function, gradient_function,	1✔
69	variable_bounds, initial_point)
70	snobfit_settings = {	1✔
71	'maxmp': self._maxmp,
72	'maxfail': self._maxfail,
73	'verbose': self._verbose,
74	}
75	options = optset(optin=snobfit_settings)	1✔
76	# counters the error when initial point is outside the acceptable bounds
77	for idx, theta in enumerate(initial_point):	1✔
78	if abs(theta) > variable_bounds[idx][0]:	1✔
79	initial_point[idx] = initial_point[idx] % variable_bounds[idx][0]	1✔
80	elif abs(theta) > variable_bounds[idx][1]:	×
81	initial_point[idx] = initial_point[idx] % variable_bounds[idx][1]	×
82	res, history = skq.minimize(objective_function, np.array(initial_point, dtype=float),	1✔
83	bounds=variable_bounds, budget=self._maxiter,
84	method="snobfit", options=options)
85	return res.optpar, res.optval, len(history)	1✔

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