openjij.sampler.sa_sampler

openjij.sampler.sa_sampler#

Classes#

SASampler

Sampler with Simulated Annealing (SA).

Functions#

`geometric_hubo_beta_schedule`(sa_system, beta_max, ...)
`geometric_ising_beta_schedule`(cxxgraph[, beta_max, ...])	Make geometric cooling beta schedule.

Module Contents#

class openjij.sampler.sa_sampler.SASampler[source]#

Bases: openjij.sampler.sampler.BaseSampler

Inheritance diagram of openjij.sampler.sa_sampler.SASampler

Sampler with Simulated Annealing (SA).

Parameters:

beta_min (float) – Minmum beta (inverse temperature). You can overwrite in methods .sample_*.
beta_max (float) – Maximum beta (inverse temperature). You can overwrite in methods .sample_*.
num_reads (int) – number of sampling (algorithm) runs. defaults None. You can overwrite in methods .sample_*.
num_sweeps (int) – number of MonteCarlo steps during SA. defaults None. You can overwrite in methods .sample_*.
schedule_info (dict) – Information about an annealing schedule.

Raises:

ValueError – If schedules or variables violate as below.
- not list or numpy.array. –
- not list of tuple (beta – float, step_length : int).
- beta is less than zero. –

remove_unknown_kwargs(**kwargs) → Dict[str, Any]#

Remove with warnings any keyword arguments not accepted by the sampler.

Parameters:: **kwargs – Keyword arguments to be validated.
Return type:: Dict[str, Any]

Returns: Updated kwargs dict.

Examples

>>> import warnings
>>> sampler = dimod.RandomSampler()
>>> with warnings.catch_warnings():
...     warnings.filterwarnings('ignore')
...     try:
...         sampler.remove_unknown_kwargs(num_reads=10, non_param=3)
...     except dimod.exceptions.SamplerUnknownArgWarning:
...        pass
{'num_reads': 10}

Sample Ising model.

Parameters:

bqm (openjij.model.model.BinaryQuadraticModel)
beta_min (float) – minimal value of inverse temperature
beta_max (float) – maximum value of inverse temperature
num_sweeps (int) – number of sweeps
num_reads (int) – number of reads
schedule (list) – list of inverse temperature
initial_state (dict) – initial state
updater (str) – updater algorithm
sparse (bool) – use sparse matrix or not.
reinitialize_state (bool) – if true reinitialize state for each run
seed (int) – seed for Monte Carlo algorithm

Returns:

results

Return type:

openjij.sampler.response.Response

Examples

for Ising case:

>>> h = {0: -1, 1: -1, 2: 1, 3: 1}
>>> J = {(0, 1): -1, (3, 4): -1}
>>> sampler = openj.SASampler()
>>> 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 = openj.SASampler()
>>> res = sampler.sample_qubo(Q)

sample_hubo(J: dict[tuple, float], vartype: str | None = None, num_sweeps: int = 1000, num_reads: int = 1, num_threads: int = 1, beta_min: float | None = None, beta_max: float | None = None, updater: str = 'METROPOLIS', random_number_engine: str = 'XORSHIFT', seed: int | None = None, temperature_schedule: str = 'GEOMETRIC')[source]#

Sampling from higher order unconstrainted binary optimization.

Parameters:

J (dict) – Interactions.
vartype (str) – “SPIN” or “BINARY”.
num_sweeps (int, optional) – The number of sweeps. Defaults to 1000.
num_reads (int, optional) – The number of reads. Defaults to 1.
num_threads (int, optional) – The number of threads. Parallelized for each sampling with num_reads > 1. Defaults to 1.
beta_min (float, optional) – Minimum beta (initial inverse temperature). Defaults to None.
beta_max (float, optional) – Maximum beta (final inverse temperature). Defaults to None.
updater (str, optional) – Updater. One can choose “METROPOLIS”, “HEAT_BATH”, or “k-local”. Defaults to “METROPOLIS”.
random_number_engine (str, optional) – Random number engine. One can choose “XORSHIFT”, “MT”, or “MT_64”. Defaults to “XORSHIFT”.
seed (int, optional) – seed for Monte Carlo algorithm. Defaults to None.
temperature_schedule (str, optional) – Temperature schedule. One can choose “LINEAR”, “GEOMETRIC”. Defaults to “GEOMETRIC”.

Returns:

results

Return type:

openjij.sampler.response.Response

Examples::

for Ising case::

>>> sampler = openjij.SASampler()
>>> J = {(0,): -1, (0, 1): -1, (0, 1, 2): 1}
>>> response = sampler.sample_hubo(J, "SPIN")

for Binary case::

>>> sampler = ooenjij.SASampler()
>>> J = {(0,): -1, (0, 1): -1, (0, 1, 2): 1}
>>> response = sampler.sample_hubo(J, "BINARY")

sample_ising(h, J, **parameters)#

Sample from an Ising model using the implemented sample method.

Parameters:

h (dict) – Linear biases
J (dict) – Quadratic biases

Returns:

results

Return type:

openjij.sampler.response.Response

sample_qubo(Q, **parameters)#

Sample from a QUBO model using the implemented sample method.

Parameters:: Q (dict or numpy.ndarray) – Coefficients of a quadratic unconstrained binary optimization
Returns:: results
Return type:: openjij.sampler.response.Response

property parameters#
Parameters as a dict, where keys are keyword parameters accepted by the
sampler methods and values are lists of the properties relevent to each
parameter.

properties#

openjij.sampler.sa_sampler.geometric_hubo_beta_schedule(sa_system, beta_max, beta_min, num_sweeps)[source]#

openjij.sampler.sa_sampler.geometric_ising_beta_schedule(cxxgraph: Dense | CSRSparse, beta_max=None, beta_min=None, num_sweeps=1000)[source]#

Make geometric cooling beta schedule.

Parameters:

cxxgraph (Union[openjij.cxxjij.graph.Dense, openjij.cxxjij.graph.CSRSparse]) – Ising graph, must be either Dense or CSRSparse.
beta_max (float, optional) – [description]. Defaults to None.
beta_min (float, optional) – [description]. Defaults to None.
num_sweeps (int, optional) – [description]. Defaults to 1000.

Returns:

list of cxxjij.utility.ClassicalSchedule, list of beta range [max, min]