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Jij Swap Moving Sampler

NHOT_CONSTRAINTS = typ.List[typ.Tuple[typ.List[int], int]] module-attribute

N-hot constraint indices.

x0 + x1 + x2 = 3, x3 + x4 + x5 = 1 -> [([0, 1, 2], 3), ([3, 4, 5], 1)]

JijSwapMovingParameters dataclass

Manage Parameters for using JijSwapMovingSampler.

Attributes:

Name Type Description
constraints Optional[list]

Constraint term. x0+x1=1 and x2+x3=1 is written as [([0,1], 1), ([2,3], 1)].
penalties Optional[list]

Penalty term. 1.3(0.1x1+0.2x2+0.3x3-1)^2 is written as [(1.3, {1:0.1, 2:0.2, 3:0.3}, 1)].
beta_min Optional[float]

Minimum (initial) inverse temperature. If None, this will be set automatically.
beta_max Optional[float]

Maximum (final) inverse temperature. If None, this will be set automatically.
num_sweeps Optional[int]

The number of Monte-Carlo steps. If None, 1000 will be set.
num_reads Optional[int]

The number of samples. If None, 1 will be set.
initial_state Optional[dict]

Initial state. If None, this will be set automatically.
updater Optional[str]

Updater algorithm. "single spin flip" or "swendsen wang". If None, "single spin flip" will be set.
sparse Optional[bool]

If True, only non-zero matrix elements are stored, which will save memory. If None, False will be set.
reinitialize_state Optional[bool]

If True, reinitialize state for each run. If None, True will be set.
seed Optional[int]

Seed for Monte Carlo algorithm. If None, this will be set automatically.

JijSwapMovingSampler

Bases: JijZeptBaseSampler

A sampler class for JijSwapMoving.

parameters property

Returns a dictionary of parameters for the model.

properties property

Returns a dictionary of properties for the model.

__init__(token=None, url=None, proxy=None, config=None, config_env='default')

Sets token and url.

Parameters:

Name Type Description Default
token str

Token string. Defaults to None.

 None
url Union[str, dict]

API URL. Defaults to None.

 None
proxy str

Proxy URL. Defaults to None.

 None
config str

Config file path. Defaults to None.

 None

Raises:

Type Description

obj:TypeError: token, url, or config is not str.

sample_hubo(J, parameters=None, max_wait_time=None, sync=True, queue_name=None, **kwargs)

The simulated annealing is performed for higher ordered unconstraind.

binary optimizations (hubo) with the linear constraint conditions. Note here that the linear constraint conditions are strictly satisfied.

To configure the solver, instantiate the JijSwapMovingParameters class and pass the instance to the parameters argument.

Parameters:

Name Type Description Default
J Dict

Polynomial interactions.

 required
parameters JijSwapMovingParameters | None

defaults None.

 None
max_wait_time int | float | None

The number of timeout [sec] for post request. If None, 60 (one minute) will be set. Please note that this argument is for the jijzept timeout and not for configuring solver settings, such as solving time.

 None
sync bool

Synchronous mode.

 True
queue_name Optional[str]

Queue name.

 None
**kwargs

SwapMoving parameters using **kwargs. If both **kwargs and parameters are exist, the value of **kwargs takes precedence.

 {}

Returns:

Type Description
JijModelingResponse

dimod.SampleSet: Stores minimum energy samples and other information.

Examples:

import jijzept as jz
sampler = jz.JijSwapMovingSampler(config='config.toml')
response = sampler.sample_hubo(J={(0,1,2,3,4): -1}, vartype="SPIN",
                               constraints=[([0,1], 0), ([2,3,4], 1)])

sample_model(problem, feed_dict, multipliers={}, fixed_variables={}, needs_square_dict=None, search=False, num_search=15, algorithm=None, parameters=None, max_wait_time=None, sync=True, queue_name=None, **kwargs)

Sample using JijModeling by means of the simulated annealing.

To configure the solver, instantiate the JijSwapMovingParameters class and pass the instance to the parameters argument.

Parameters:

Name Type Description Default
problem Problem

Mathematical expression of JijModeling. The decision variable type should be Binary.

 required
feed_dict Dict[str, Union[Number, list, np.ndarray]]

The actual values to be assigned to the placeholders.

 required
multipliers Dict[str, Number]

The actual multipliers for penalty terms, derived from constraint conditions.

 {}
fixed_variables Dict[str, Dict[Tuple[int, ...], Union[int, float]]]

dictionary of variables to fix.

 {}
needs_square_dict Dict[str, bool]

If True, the corresponding constraint is squared when added as a penalty to the QUBO. When the constraint label is not added to the 'needs_square_dict', it defaults to True for linear constraints and False for non-linear constraints.

 None
search bool

If True, the parameter search will be carried out, which tries to find better values of multipliers for penalty terms.

 False
num_search int

The number of parameter search iteration. Defaults to set 15. This option works if search is True.

 15
algorithm Optional[str]

Algorithm for parameter search. Defaults to None.

 None
parameters JijSwapMovingParameters | None

defaults None.

 None
max_wait_time int | float | None

The number of timeout [sec] for post request. If None, 60 (one minute) will be set. Please note that this argument is for the jijzept timeout and not for configuring solver settings, such as solving time.

 None
sync bool

Synchronous mode.

 True
queue_name Optional[str]

Queue name.

 None
**kwargs

SwapMoving parameters using **kwargs. If both **kwargs and parameters are exist, the value of **kwargs takes precedence.

 {}

Returns:

Name Type Description
JijModelingResponse JijModelingResponse

Stores minimum energy samples and other information.

Examples:

import jijzept as jz
import jijmodeling as jm
n = jm.Placeholder('n')
x = jm.Binary('x', shape=n)
d = jm.Placeholder('d', shape=n)
i = jm.Element("i", n)
problem = jm.Problem('problem')
problem += jm.Sum(i, d[i] * x[i])
problem += jm.Constraint("one-hot", jm.Sum(i, x[i]) == 1)
sampler = jz.JijSwapMovingSampler(config='config.toml')
response = sampler.sample_model(problem, feed_dict={'n': 5, 'd': [1,2,3,4,5]})

sample_qubo(Q, parameters=None, max_wait_time=None, sync=True, queue_name=None, **kwargs)

The simulated annealing is performed for binary quadratic models with.

the linear constraint conditions. Note here that the linear constraint conditions are strictly satisfied.

To configure the solver, instantiate the JijSwapMovingParameters class and pass the instance to the parameters argument.

Parameters:

Name Type Description Default
Q Dict

The linear or quadratic terms.

 required
parameters JijSwapMovingParameters | None

defaults None.

 None
max_wait_time int | float | None

The number of timeout [sec] for post request. If None, 60 (one minute) will be set. Please note that this argument is for the jijzept timeout and not for configuring solver settings, such as solving time.

 None
sync bool

Synchronous mode.

 True
queue_name Optional[str]

Queue name.

 None
**kwargs

SwapMoving parameters using **kwargs. If both **kwargs and parameters are exist, the value of **kwargs takes precedence.

 {}

Returns:

Type Description
JijModelingResponse

dimod.SampleSet: Stores minimum energy samples and other information.

Examples:

import jijzept as jz
sampler = jz.JijSwapMovingSampler(config='config.toml')
response = sampler.sample_qubo(Q={(0,0): -0.5, (0,1): -1, (0,2): -1, (2,3): -1},
                               constraints=[([0,1], 1), ([2,3], 1)])

sample_nhot(is_hubo, client, solver, queue_name, qubo, constant, max_wait_time, sync, constraints, **kwargs)

Sample from a QUBO or Ising Hamiltonian with NHOT constraints using D-Wave's quantum annealer.

Parameters:

Name Type Description Default
is_hubo bool

Whether the input problem is a HUBO or not.

 required
client JijZeptClient

A JijZeptClient instance.

 required
solver str

The name of the D-Wave solver to use.

 required
queue_name str

The name of the D-Wave queue to use.

 required
qubo dict[tuple[int, int], float]

The input QUBO or Ising Hamiltonian.

 required
constant float

The constant offset for the Hamiltonian.

 required
max_wait_time int | float | None

Maximum time to wait for the solver in seconds. If None, waits indefinitely.

 required
sync bool

Whether to wait for the results to complete before returning.

 required
constraints NHOT_CONSTRAINTS

The NHOT constraints to apply to the problem.

 required
**kwargs

Additional keyword arguments to pass to sample_model.

 {}

Returns:

Name Type Description
SampleSet

The sample set obtained from the solver.
Last update: 2023年5月24日