openjij.sampler.chimera_gpu.gpu_sqa_sampler

Module Contents

Classes

GPUChimeraSQASampler

Sampler with Simulated Quantum Annealing (SQA) on GPU.

class openjij.sampler.chimera_gpu.gpu_sqa_sampler.GPUChimeraSQASampler(beta=10.0, gamma=1.0, trotter=4, num_sweeps=100, schedule=None, num_reads=1, unit_num_L=None)

Bases: openjij.sampler.sqa_sampler.SQASampler, openjij.sampler.chimera_gpu.base_gpu_chimera.BaseGPUChimeraSampler

Sampler with Simulated Quantum Annealing (SQA) on GPU.

Inherits from openjij.sampler.sqa_sampler.SQASampler.

Parameters:

• 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.

property adjacency: Dict[dimod.typing.Variable, Set]

Adjacency structure formatted as a dict, where keys are the nodes of the structured sampler and values are sets of all adjacent nodes for each key node.

Return type:

Dict[dimod.typing.Variable, Set]

property edgelist

Edges/interactions allowed by the sampler.

property nodelist

Nodes/variables allowed by the sampler.

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.

property structure: _Structure

Structure of the structured sampler formatted as a namedtuple() where the 3-tuple values are the nodelist, edgelist and adjacency attributes.

Return type:

_Structure

properties

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(bqm: Union[openjij.model.model.BinaryQuadraticModel, dimod.BinaryQuadraticModel], beta: Optional[float] = None, gamma: Optional[float] = None, num_sweeps: Optional[int] = None, schedule: Optional[list] = None, trotter: Optional[int] = None, num_reads: Optional[int] = None, initial_state: Optional[Union[list, dict]] = None, updater: Optional[str] = None, sparse: Optional[bool] = None, reinitialize_state: Optional[bool] = None, seed: Optional[int] = None) → Response

Sampling from the Ising model.

Parameters:

• bqm (openjij.BinaryQuadraticModel) –

• 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.

• trotter (int) – Trotter number.

• 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’.

• sparse (bool) – use sparse matrix or not.

• reinitialize_state (bool, optional) – Re-initilization at each sampling. Defaults to True.

• seed (int, optional) – Sampling seed. Defaults to None.

Raises:

ValueError –

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 = openjij.SQASampler()
>>> 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 = openjij.SQASampler()
>>> res = sampler.sample_qubo(Q)

sample_ising(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.

Parameters:

• 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:

results

Return type:

openjij.sampler.response.Response

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)

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

to_networkx_graph()

Convert structure to NetworkX graph format.

Note that NetworkX must be installed for this method to work.

Returns:

A NetworkX graph containing the nodes and edges from the sampler’s structure.

Return type:

networkx.Graph

valid_bqm_graph(bqm: dimod.BinaryQuadraticModel) → bool

Validate that problem defined by dimod.BinaryQuadraticModel matches the graph provided by the sampler.

Parameters:

bqm (dimod.BinaryQuadraticModel) – dimod.BinaryQuadraticModel object to validate.

Returns:

Boolean indicating validity of BQM graph compared to sampler graph.

Return type:

bool