# 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ============================================================================
"""Coherent Ising Machine with chaotic amplitude control algorithm."""
# pylint: disable=invalid-name
import numpy as np
from scipy.sparse import csr_matrix
from .QAIA import QAIA
[文档]class CAC(QAIA):
r"""
Coherent Ising Machine with chaotic amplitude control algorithm.
Reference: `Coherent Ising machines with optical error correction
circuits <https://onlinelibrary.wiley.com/doi/full/10.1002/qute.202100077>`_.
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: ``0.075``.
"""
# pylint: disable=too-many-arguments,too-many-instance-attributes
def __init__(
self,
J,
h=None,
x=None,
n_iter=1000,
batch_size=1,
dt=0.075,
):
"""Construct CAC algorithm."""
super().__init__(J, h, x, n_iter, batch_size)
self.J = csr_matrix(self.J)
self.N = self.J.shape[0]
self.dt = dt
# The number of first iterations
self.Tr = int(0.9 * self.n_iter)
# The number of additional iterations
self.Tp = self.n_iter - self.Tr
# pumping parameters
self.p = np.hstack([np.linspace(-0.5, 1, self.Tr), np.ones(self.Tp)])
# target amplitude
self.alpha = np.hstack([np.linspace(1, 3, self.Tr), 3.0 * np.ones(self.Tp)])
# coupling strength
self.xi = np.sqrt(2 * self.N / np.sum(self.J**2))
# rate of change of error variables
self.beta = 0.3
self.initialize()
[文档] def initialize(
self,
):
"""Initialize spin values and error variables."""
if self.x is None:
self.x = np.random.normal(0, 10 ** (-4), size=(self.N, self.batch_size))
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}")
self.e = np.ones((self.N, self.batch_size))
# pylint: disable=attribute-defined-outside-init
[文档] def update(self):
"""Dynamical evolution."""
for i in range(self.n_iter):
if self.h is None:
self.x = (
self.x + (-self.x**3 + (self.p[i] - 1) * self.x + self.xi * self.e * (self.J @ self.x)) * self.dt
)
else:
self.x = (
self.x
+ (-self.x**3 + (self.p[i] - 1) * self.x + self.xi * self.e * (self.J @ self.x + self.h))
* self.dt
)
self.e = self.e + (-self.beta * self.e * (self.x**2 - self.alpha[i])) * self.dt
cond = np.abs(self.x) > (1.5 * np.sqrt(self.alpha[i]))
self.x = np.where(cond, 1.5 * np.sign(self.x) * np.sqrt(self.alpha[i]), self.x)