openjij.sampler.chimera_gpu#

Submodules#

Package Contents#

Classes#

GPUChimeraSASampler

Sampler with Simulated Annealing (SA) on GPU.

GPUChimeraSQASampler

Sampler with Simulated Quantum Annealing (SQA) on GPU.
class openjij.sampler.chimera_gpu.GPUChimeraSASampler(beta_min=None, beta_max=None, num_sweeps=1000, schedule=None, num_reads=1, unit_num_L=None)[source]#

Bases: openjij.sampler.sa_sampler.SASampler, openjij.sampler.chimera_gpu.base_gpu_chimera.BaseGPUChimeraSampler

Inheritance diagram of openjij.sampler.chimera_gpu.GPUChimeraSASampler

Sampler with Simulated Annealing (SA) on GPU.

Inherits from openjij.sampler.sampler.BaseSampler.

Parameters:

  • beta_min (float) – Minimum inverse temperature.

  • beta_max (float) – Maximum inverse temperature.

  • num_sweeps (int) – Length of Monte Carlo step.

  • 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[openj.model.model.BinaryQuadraticModel, dimod.BinaryQuadraticModel], beta_min: Optional[float] = None, beta_max: Optional[float] = None, num_sweeps: Optional[int] = None, num_reads: Optional[int] = None, schedule: Optional[list] = 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#

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: Union[dict, openj.model.model.BinaryPolynomialModel, cimod.BinaryPolynomialModel], vartype: Optional[str] = None, beta_min: Optional[float] = None, beta_max: Optional[float] = None, num_sweeps: Optional[int] = None, num_reads: Optional[int] = None, schedule: Optional[list] = None, initial_state: Optional[Union[list, dict]] = None, updater: Optional[str] = None, reinitialize_state: Optional[bool] = None, seed: Optional[int] = None) → Response#

Sampling from higher order unconstrainted binary optimization.

Parameters:

  • J (dict) – Interactions.

  • vartype (str, openjij.VarType) – “SPIN” or “BINARY”.

  • beta_min (float, optional) – Minimum beta (initial inverse temperature). Defaults to None.

  • beta_max (float, optional) – Maximum beta (final inverse temperature). Defaults to None.

  • schedule (list, optional) – schedule list. Defaults to None.

  • num_sweeps (int, optional) – number of sweeps. Defaults to None.

  • num_reads (int, optional) – number of reads. Defaults to 1.

  • init_state (list, optional) – initial state. Defaults to None.

  • reinitialize_state (bool) – if true reinitialize state for each run

  • seed (int, optional) – seed for Monte Carlo algorithm. Defaults to None.

  • initial_state (Optional[Union[list, dict]]) –

  • updater (Optional[str]) –

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, beta_min=None, beta_max=None, num_sweeps=None, num_reads=1, schedule=None, initial_state=None, updater=None, reinitialize_state=True, seed=None, unit_num_L=None)[source]#

Sample with Ising model.

Parameters:

  • h (dict) – linear biases

  • J (dict) – quadratic biases

  • 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

  • reinitialize_state (bool) – if true reinitialize state for each run

  • seed (int) – seed for Monte Carlo algorithm

  • unit_num_L (int) – number of chimera units

Returns:

results

Return type:

openjij.sampler.response.Response

Examples:

>>> sampler = openjij.sampler.chimera_gpu.gpu_sa_sampler.GPUChimeraSASampler(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

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

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

Inheritance diagram of openjij.sampler.chimera_gpu.GPUChimeraSQASampler

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)[source]#

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