from __future__ import annotations
import numpy as np
import openjij
import openjij as oj
import openjij.cxxjij as cxxjij
from openjij.sampler.sqa_sampler import SQASampler
[docs]
class CSQASampler(SQASampler):
"""Sampler with continuous-time simulated quantum annealing (CSQA) using
Hamiltonian.
.. math::
H(s) = s H_p + \\Gamma (1-s)\\sum_i \\sigma_i^x
where :math:`H_p` is the problem Hamiltonian we want to solve.
Args:
beta (float): Inverse temperature.
gamma (float): Amplitude of quantum fluctuation.
schedule (list): schedule list
step_num (int): Number of Monte Carlo step.
schedule_info (dict): Information about a annealing schedule.
num_reads (int): Number of iterations.
num_sweeps (int): number of sweeps
schedule_info (dict): Information about a annealing schedule.
"""
def __init__(
self, beta=5.0, gamma=1.0, num_sweeps=1000, schedule=None, num_reads=1
):
self._default_params = {
"beta": beta,
"gamma": gamma,
"num_sweeps": num_sweeps,
"schedule": schedule,
"num_reads": num_reads,
}
self._params = {
"beta": beta,
"gamma": gamma,
"num_sweeps": num_sweeps,
"schedule": schedule,
"num_reads": num_reads,
}
def _get_result(self, system, model):
info = {}
info["spin_config"] = system.spin_config
state = cxxjij.result.get_solution(system)
return state, info
[docs]
def sample_ising(
self,
h,
J,
beta:float=5.0,
gamma:float = 1.0,
num_sweeps: int=1000,
schedule=None,
num_reads:int=1,
initial_state=None,
updater=None,
reinitialize_state=True,
seed=None,
):
"""Sampling from the Ising model.
Args:
h (dict): linear biases
J (dict): quadratic biases
beta (float, optional): inverse temperature
gamma (float, optional): strength of transverse field
num_sweeps (int, optional): number of sampling.
schedule (list, optional): schedule list
num_reads (int, optional): number of iterations
initial_state (optional): initial state of spins
updater (str, optional): updater algorithm
reinitialize_state (bool, optional): Re-initilization at each sampling. Defaults to True.
seed (int, optional): Sampling seed.
Returns:
:class:`openjij.sampler.response.Response`: results
Examples:
for Ising case::
>>> h = {0: -1, 1: -1, 2: 1, 3: 1}
>>> J = {(0, 1): -1, (3, 4): -1}
>>> sampler = openjij.CSQASampler()
>>> res = sampler.sample_ising(h, J)
for QUBO case::
>>> Q = {(0, 0): -1, (1, 1): -1, (2, 2): 1, (3, 3): 1, (4, 4): 1, (0, 1): -1, (3, 4): 1}
>>> sampler = openjijj.CSQASampler()
>>> res = sampler.sample_qubo(Q)
"""
# Set default updater
if updater is None:
updater = "swendsenwang"
bqm = oj.model.model.BinaryQuadraticModel(
linear=h, quadratic=J, vartype="SPIN", sparse=True
)
# Continuous time ising system only supports sparse ising graph
ising_graph = bqm.get_cxxjij_ising_graph()
self._set_params(
beta=beta, gamma=gamma, num_sweeps=num_sweeps, num_reads=num_reads
)
self._annealing_schedule_setting(
bqm,
self._params["beta"],
self._params["gamma"],
self._params["num_sweeps"],
self._params["schedule"],
)
# make init state generator --------------------------------
if initial_state is None:
def init_generator():
spin_config = np.random.choice([1, -1], len(bqm.variables))
return list(spin_config)
else:
def init_generator():
return initial_state
# -------------------------------- make init state generator
# choose updater -------------------------------------------
sqa_system = cxxjij.system.make_continuous_time_ising(
init_generator(), ising_graph[0], self._params["gamma"]
)
_updater_name = updater.lower().replace("_", "").replace(" ", "")
if _updater_name == "swendsenwang":
algorithm = cxxjij.algorithm.Algorithm_ContinuousTimeSwendsenWang_run
else:
raise ValueError('updater is one of "swendsen wang"')
# ------------------------------------------- choose updater
response = self._cxxjij_sampling(
bqm, init_generator, algorithm, sqa_system, reinitialize_state, seed
)
response.info["schedule"] = self.schedule_info
return response
