Inheritance diagram for openjij.sampler.csqa_sampler.CSQASampler:

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Collaboration diagram for openjij.sampler.csqa_sampler.CSQASampler:

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Public Member Functions
	__init__ (self, beta=5.0, gamma=1.0, num_sweeps=1000, schedule=None, num_reads=1)

	sample_ising (self, h, J, float beta=5.0, float gamma=1.0, int num_sweeps=1000, schedule=None, int num_reads=1, initial_state=None, updater=None, reinitialize_state=True, seed=None)

Public Member Functions inherited from openjij.sampler.sqa_sampler.SQASampler
	parameters (self)

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

Public Member Functions inherited from openjij.sampler.sampler.BaseSampler
	sample_qubo (self, Q, **parameters)

Protected Member Functions
	_get_result (self, system, model)

Protected Member Functions inherited from openjij.sampler.sqa_sampler.SQASampler
	_convert_validation_schedule (self, schedule, beta)

	_annealing_schedule_setting (self, model, beta=None, gamma=None, num_sweeps=None, schedule=None)

Protected Member Functions inherited from openjij.sampler.sampler.BaseSampler
	_set_params (self, **kwargs)

	_sampling (self, **kwargs)

	_cxxjij_sampling (self, model, init_generator, algorithm, system, reinitialize_state=None, seed=None, offset=None)

Protected Attributes
	_default_params

Protected Attributes inherited from openjij.sampler.sqa_sampler.SQASampler
	_default_params

	_params

	_make_system

	_algorithm

Static Protected Attributes
	_params

Additional Inherited Members
Public Attributes inherited from openjij.sampler.sqa_sampler.SQASampler
	schedule_info

Public Attributes inherited from openjij.sampler.sampler.BaseSampler
	sample_qubo

Static Public Attributes inherited from openjij.sampler.sampler.BaseSampler
	properties = dict()

Detailed Description

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.

Constructor & Destructor Documentation

◆ init()

openjij.sampler.csqa_sampler.CSQASampler.__init__	(	self,
		beta = `5.0`,
		gamma = `1.0`,
		num_sweeps = `1000`,
		schedule = `None`,
		num_reads = `1`
	)

Reimplemented from openjij.sampler.sqa_sampler.SQASampler.

Member Function Documentation

◆ _get_result()

openjij.sampler.csqa_sampler.CSQASampler._get_result	(	self,
		system,
		model
	)

protected

Reimplemented from openjij.sampler.sqa_sampler.SQASampler.

◆ sample_ising()

openjij.sampler.csqa_sampler.CSQASampler.sample_ising	(		self,
			h,
			J,
		float	beta = `5.0`,
		float	gamma = `1.0`,
		int	num_sweeps = `1000`,
			schedule = `None`,
		int	num_reads = `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)

Reimplemented from openjij.sampler.sampler.BaseSampler.

References openjij.sampler.sqa_sampler.SQASampler._annealing_schedule_setting(), openjij.sampler.sampler.BaseSampler._cxxjij_sampling(), openjij.sampler.csqa_sampler.CSQASampler._params, openjij.sampler.sa_sampler.SASampler._params, openjij.sampler.sqa_sampler.SQASampler._params, openjij.sampler.sampler.BaseSampler._set_params(), openjij.sampler.sa_sampler.SASampler.schedule_info, and openjij.sampler.sqa_sampler.SQASampler.schedule_info.

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