# Copyright 2023 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""Simulated Coherent Ising Machine."""
# pylint: disable=invalid-name
import numpy as np
from scipy.sparse import csr_matrix
from .QAIA import QAIA
[docs]class SimCIM(QAIA):
r"""
Simulated Coherent Ising Machine.
Reference: `Annealing by simulating the coherent Ising
machine <https://opg.optica.org/oe/fulltext.cfm?uri=oe-27-7-10288&id=408024>`_.
Args:
J (Union[numpy.array, csr_matrix]): The coupling matrix with shape :math:`(N x N)`.
h (numpy.array): The external field with shape :math:`(N, )`.
x (numpy.array): The initialized spin value with shape :math:`(N x batch_size)`. Default: ``None``.
n_iter (int): The number of iterations. Default: ``1000``.
batch_size (int): The number of sampling. Default: ``1``.
dt (float): The step size. Default: ``1``.
momentum (float): momentum factor. Default: ``0.9``.
sigma (float): The standard deviation of noise. Default: ``0.03``.
pt (float): Pump parameter. Default: ``6.5``.
"""
# pylint: disable=too-many-arguments, too-many-instance-attributes
def __init__(
self,
J,
h=None,
x=None,
n_iter=1000,
batch_size=1,
dt=0.01,
momentum=0.9,
sigma=0.03,
pt=6.5,
):
"""Construct SimCIM algorithm."""
super().__init__(J, h, x, n_iter, batch_size)
self.J = csr_matrix(self.J)
self.dt = dt
self.momentum = momentum
self.sigma = sigma
self.pt = pt
self.initialize()
[docs] def initialize(self):
"""Initialize spin."""
# Initialization of spin value
if self.x is None:
self.x = np.zeros((self.N, self.batch_size))
# gradient
self.dx = np.zeros_like(self.x)
if self.x.shape[0] != self.N:
raise ValueError(f"The size of x {self.x.shape[0]} is not equal to the number of spins {self.N}")
# pump-loss factor
self.p_list = (np.tanh(np.linspace(-3, 3, self.n_iter)) - 1) * self.pt
# pylint: disable=attribute-defined-outside-init
[docs] def update(self):
"""Dynamical evolution."""
for _, p in zip(range(self.n_iter), self.p_list):
if self.h is None:
newdc = self.x * p + (
self.J.dot(self.x) * self.dt + np.random.normal(size=(self.N, self.batch_size)) * self.sigma
)
else:
newdc = self.x * p + (
(self.J.dot(self.x) + self.h) * self.dt
+ np.random.normal(size=(self.N, self.batch_size)) * self.sigma
)
# gradient + momentum
self.dx = self.dx * self.momentum + newdc * (1 - self.momentum)
ind = (np.abs(self.x + self.dx) < 1.0).astype(np.int64)
self.x += self.dx * ind