Source code for openjij.sampler.chimera_gpu.gpu_sqa_sampler

# Copyright 2019 Jij Inc.

# 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


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import cxxjij
import openjij
from openjij.sampler import SQASampler
from .base_gpu_chimera import BaseGPUChimeraSampler
from openjij.model import BinaryQuadraticModel, ChimeraModel
import numpy as np

[docs]class GPUChimeraSQASampler(SQASampler, BaseGPUChimeraSampler): """Sampler with Simulated Quantum Annealing (SQA) on GPU. Inherits from :class:`openjij.sampler.sqa_sampler.SQASampler`. Args: beta (float): Inverse temperature. gamma (float): Amplitude of quantum fluctuation. trotter (int): Trotter number. num_sweeps (int): number of sweeps schedule_info (dict): Information about a annealing schedule. num_reads (int): Number of iterations. unit_num_L (int): Length of one side of two-dimensional lattice in which chimera unit cells are arranged. Raises: ValueError: If variables violate as below. - trotter number is odd. - no input "unit_num_L" to an argument or this constructor. - given problem graph is incompatible with chimera graph. AttributeError: If GPU doesn't work. """ def __init__(self, beta=10.0, gamma=1.0, trotter=4, num_sweeps=100, schedule=None, num_reads=1, unit_num_L=None): # GPU Sampler allows only even trotter number if trotter % 2 != 0: raise ValueError('GPU Sampler allows only even trotter number') self.trotter = trotter self.unit_num_L = unit_num_L super().__init__(beta=beta, gamma=gamma, trotter=trotter, num_reads=num_reads, num_sweeps=num_sweeps, schedule=schedule) self._make_system = { 'singlespinflip': cxxjij.system.make_chimera_transverse_gpu } self._algorithm = { 'singlespinflip': cxxjij.algorithm.Algorithm_GPU_run } def _get_result(self, system, model): result = cxxjij.result.get_solution(system) sys_info = {} return result, sys_info
[docs] def sample_ising(self, h, J, beta=None, gamma=None, num_sweeps=None, schedule=None,num_reads=1, unit_num_L=None, initial_state=None, updater='single spin flip', reinitialize_state=True, seed=None): """Sampling from the Ising model Args: h (dict): Linear term of the target Ising model. J (dict): Quadratic term of the target Ising model. beta (float, optional): inverse tempareture. gamma (float, optional): strangth of transverse field. Defaults to None. num_sweeps (int, optional): number of sweeps. Defaults to None. schedule (list[list[float, int]], optional): List of annealing parameter. Defaults to None. num_reads (int, optional): number of sampling. Defaults to 1. initial_state (list[int], optional): Initial state. Defaults to None. updater (str, optional): update method. Defaults to 'single spin flip'. reinitialize_state (bool, optional): Re-initilization at each sampling. Defaults to True. seed (int, optional): Sampling seed. Defaults to None. Returns: :class:`openjij.sampler.response.Response`: results Examples:: >>> sampler = oj.GPUChimeraSQASampler(unit_num_L=2) >>> h = {0: -1, 1: -1, 2: 1, 3: 1}, >>> J = {(0, 4): -1, (2, 5): -1} >>> res = sampler.sample_ising(h, J) """ self.unit_num_L = unit_num_L if unit_num_L else self.unit_num_L model = openjij.ChimeraModel(linear=h, quadratic=J, var_type='SPIN', unit_num_L=self.unit_num_L, gpu=True) # define Chimera structure structure = {} structure['size'] = 8 * self.unit_num_L * self.unit_num_L structure['dict'] = {} if isinstance(model.indices[0], int): # identity dict for ind in model.indices: structure['dict'][ind] = ind elif isinstance(model.indices[0], tuple): # map chimera coordinate to index for ind in model.indices: structure['dict'][ind] = model.to_index(*ind, self.unit_num_L) return self._sampling(model, beta=beta, gamma=gamma, num_sweeps=num_sweeps, schedule=schedule, num_reads=num_reads, initial_state=initial_state, updater=updater, reinitialize_state=reinitialize_state, seed=seed, structure=structure)