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* add NFT optimizer * Add specific unit test for NFT Co-authored-by: Manoel Marques <[email protected]> Co-authored-by: Steve Wood <[email protected]> Co-authored-by: woodsp <[email protected]>
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‎qiskit/aqua/components/optimizers/__init__.py

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COBYLA
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L_BFGS_B
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NELDER_MEAD
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NFT
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P_BFGS
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POWELL
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SLSQP
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from .spsa import SPSA
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from .tnc import TNC
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from .aqgd import AQGD
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from .nft import NFT
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from .nlopts.crs import CRS
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from .nlopts.direct_l import DIRECT_L
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from .nlopts.direct_l_rand import DIRECT_L_RAND
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'COBYLA',
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'L_BFGS_B',
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'NELDER_MEAD',
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'NFT',
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'P_BFGS',
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'POWELL',
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'SLSQP',

‎qiskit/aqua/components/optimizers/nft.py

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# -*- coding: utf-8 -*-
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# This code is part of Qiskit.
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#
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# (C) Copyright IBM 2019, 2020.
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#
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# This code is licensed under the Apache License, Version 2.0. You may
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# obtain a copy of this license in the LICENSE.txt file in the root directory
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# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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#
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# Any modifications or derivative works of this code must retain this
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# copyright notice, and modified files need to carry a notice indicating
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# that they have been altered from the originals.
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"""Nakanishi-Fujii-Todo algorithm."""
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from typing import Optional
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import logging
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import numpy as np
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from scipy.optimize import minimize
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from scipy.optimize import OptimizeResult
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from .optimizer import Optimizer
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logger = logging.getLogger(__name__)
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class NFT(Optimizer):
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"""
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Nakanishi-Fujii-Todo algorithm.
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See https://arxiv.org/abs/1903.12166
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"""
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_OPTIONS = ['maxiter', 'maxfev', 'disp', 'reset_interval']
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# pylint: disable=unused-argument
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def __init__(self,
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maxiter: Optional[int] = None,
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maxfev: int = 1024,
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disp: bool = False,
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reset_interval: int = 32) -> None:
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"""
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Built out using scipy framework, for details, please refer to
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https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html.
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Args:
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maxiter: Maximum number of iterations to perform.
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maxfev: Maximum number of function evaluations to perform.
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disp: disp
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reset_interval: The minimum estimates directly once
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in ``reset_interval`` times.
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Notes:
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In this optimization method, the optimization function have to satisfy
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three conditions written in [1]_.
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References:
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.. [1] K. M. Nakanishi, K. Fujii, and S. Todo. 2019.
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Sequential minimal optimization for quantum-classical hybrid algorithms.
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arXiv preprint arXiv:1903.12166.
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"""
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super().__init__()
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for k, v in locals().items():
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if k in self._OPTIONS:
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self._options[k] = v
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def get_support_level(self):
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""" return support level dictionary """
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return {
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'gradient': Optimizer.SupportLevel.ignored,
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'bounds': Optimizer.SupportLevel.ignored,
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'initial_point': Optimizer.SupportLevel.required
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}
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def optimize(self, num_vars, objective_function, gradient_function=None,
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variable_bounds=None, initial_point=None):
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super().optimize(num_vars, objective_function, gradient_function,
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variable_bounds, initial_point)
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res = minimize(objective_function, initial_point,
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method=nakanishi_fujii_todo, options=self._options)
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return res.x, res.fun, res.nfev
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# pylint: disable=invalid-name
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def nakanishi_fujii_todo(fun, x0, args=(), maxiter=None, maxfev=1024,
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reset_interval=32, eps=1e-32, callback=None, **_):
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"""
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Find the global minimum of a function using the nakanishi_fujii_todo
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algorithm [1].
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Args:
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fun (callable): ``f(x, *args)``
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Function to be optimized. ``args`` can be passed as an optional item
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in the dict ``minimizer_kwargs``.
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This function must satisfy the three condition written in Ref. [1].
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x0 (ndarray): shape (n,)
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Initial guess. Array of real elements of size (n,),
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where 'n' is the number of independent variables.
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args (tuple, optional):
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Extra arguments passed to the objective function.
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maxiter (int):
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Maximum number of iterations to perform.
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Default: None.
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maxfev (int):
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Maximum number of function evaluations to perform.
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Default: 1024.
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reset_interval (int):
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The minimum estimates directly once in ``reset_interval`` times.
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Default: 32.
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eps (float): eps
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**_ : additional options
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callback (callable, optional):
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Called after each iteration.
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Returns:
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OptimizeResult:
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The optimization result represented as a ``OptimizeResult`` object.
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Important attributes are: ``x`` the solution array. See
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`OptimizeResult` for a description of other attributes.
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Notes:
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In this optimization method, the optimization function have to satisfy
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three conditions written in [1].
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References:
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.. [1] K. M. Nakanishi, K. Fujii, and S. Todo. 2019.
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Sequential minimal optimization for quantum-classical hybrid algorithms.
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arXiv preprint arXiv:1903.12166.
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"""
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x0 = np.asarray(x0)
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recycle_z0 = None
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niter = 0
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funcalls = 0
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while True:
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idx = niter % x0.size
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if reset_interval > 0:
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if niter % reset_interval == 0:
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recycle_z0 = None
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if recycle_z0 is None:
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z0 = fun(np.copy(x0), *args)
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funcalls += 1
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else:
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z0 = recycle_z0
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p = np.copy(x0)
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p[idx] = x0[idx] + np.pi / 2
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z1 = fun(p, *args)
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funcalls += 1
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p = np.copy(x0)
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p[idx] = x0[idx] - np.pi / 2
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z3 = fun(p, *args)
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funcalls += 1
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z2 = z1 + z3 - z0
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c = (z1 + z3) / 2
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a = np.sqrt((z0 - z2) ** 2 + (z1 - z3) ** 2) / 2
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b = np.arctan((z1 - z3) / ((z0 - z2) + eps * (z0 == z2))) + x0[idx]
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b += 0.5 * np.pi + 0.5 * np.pi * np.sign((z0 - z2) + eps * (z0 == z2))
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x0[idx] = b
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recycle_z0 = c - a
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niter += 1
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if callback is not None:
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callback(np.copy(x0))
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if maxfev is not None:
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if funcalls >= maxfev:
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break
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if maxiter is not None:
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if niter >= maxiter:
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break
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return OptimizeResult(fun=fun(np.copy(x0)), x=x0, nit=niter, nfev=funcalls, success=(niter > 1))

‎test/aqua/test_optimizer_nft.py

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# -*- coding: utf-8 -*-
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# This code is part of Qiskit.
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#
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# (C) Copyright IBM 2020
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#
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# This code is licensed under the Apache License, Version 2.0. You may
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# obtain a copy of this license in the LICENSE.txt file in the root directory
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# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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#
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# Any modifications or derivative works of this code must retain this
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# copyright notice, and modified files need to carry a notice indicating
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# that they have been altered from the originals.
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""" Test of NFT optimizer """
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from test.aqua import QiskitAquaTestCase
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from qiskit import BasicAer
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from qiskit.aqua import QuantumInstance, aqua_globals
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from qiskit.aqua.operators import WeightedPauliOperator
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from qiskit.aqua.components.variational_forms import RY
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from qiskit.aqua.components.optimizers import NFT
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from qiskit.aqua.algorithms import VQE
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class TestOptimizerNFT(QiskitAquaTestCase):
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""" Test NFT optimizer using RY with VQE """
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def setUp(self):
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super().setUp()
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self.seed = 50
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aqua_globals.random_seed = self.seed
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pauli_dict = {
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'paulis': [{"coeff": {"imag": 0.0, "real": -1.052373245772859}, "label": "II"},
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{"coeff": {"imag": 0.0, "real": 0.39793742484318045}, "label": "IZ"},
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{"coeff": {"imag": 0.0, "real": -0.39793742484318045}, "label": "ZI"},
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{"coeff": {"imag": 0.0, "real": -0.01128010425623538}, "label": "ZZ"},
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{"coeff": {"imag": 0.0, "real": 0.18093119978423156}, "label": "XX"}
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]
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}
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self.qubit_op = WeightedPauliOperator.from_dict(pauli_dict)
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def test_nft(self):
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""" Test NFT optimizer by using it """
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result = VQE(self.qubit_op,
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RY(self.qubit_op.num_qubits),
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NFT()).run(
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QuantumInstance(BasicAer.get_backend('statevector_simulator'),
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seed_simulator=aqua_globals.random_seed,
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seed_transpiler=aqua_globals.random_seed))
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self.assertAlmostEqual(result.eigenvalue.real, -1.857275, places=6)

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