Source code for openjij.sampler.chimera_gpu.gpu_sqa_sampler

# Copyright 2021 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

#     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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from __future__ import annotations
import openjij
import openjij as oj
import openjij.cxxjij as cxxjij

from openjij.sampler.chimera_gpu.base_gpu_chimera import BaseGPUChimeraSampler
from openjij.sampler.sqa_sampler import SQASampler


[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=None, unit_num_L=None, initial_state=None, updater=None, 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 = openjij.sampler.chimera_gpu.gpu_sqa_sampler.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) """ # Set default updater if updater is None: updater = "single spin flip" if num_reads is None: num_reads = 1 self.unit_num_L = unit_num_L if unit_num_L else self.unit_num_L model = oj.model.chimera_model.ChimeraModel( linear=h, quadratic=J, vartype="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, )