ommx_openjij_adapter

Classes

OMMXOpenJijSAAdapter

Sampling QUBO or HUBO with Simulated Annealing (SA) by openjij.SASampler

Functions

`decode_to_samples`(→ ommx.v1.Samples)	Convert openjij.Response to `Samples`
`response_to_samples`(→ ommx.v1.Samples)	Deprecated: renamed to `decode_to_samples()`
`sample_qubo_sa`(→ ommx.v1.Samples)	Deprecated: Use `OMMXOpenJijSAAdapter.sample()` instead

Package Contents

class OMMXOpenJijSAAdapter(ommx_instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None, uniform_penalty_weight: float | None = None, penalty_weights: dict[int, float] = {}, inequality_integer_slack_max_range: int = 32)

Sampling QUBO or HUBO with Simulated Annealing (SA) by openjij.SASampler

decode(data: openjij.Response) → Solution

decode_to_samples(data: openjij.Response) → Samples

Convert openjij.Response to Samples

There is a static method decode_to_samples() that does the same thing.

decode_to_sampleset(data: openjij.Response) → SampleSet

classmethod sample(ommx_instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None, uniform_penalty_weight: float | None = None, penalty_weights: dict[int, float] = {}, inequality_integer_slack_max_range: int = 32) → SampleSet

classmethod solve(ommx_instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None, uniform_penalty_weight: float | None = None, penalty_weights: dict[int, float] = {}, inequality_integer_slack_max_range: int = 32) → Solution

beta_max: float | None = None: maximum value of inverse temperature

beta_min: float | None = None: minimal value of inverse temperature

inequality_integer_slack_max_range: int = 32: Max range for integer slack variables in inequality constraints, passed to Instance.to_qubo or Instance.to_hubo

initial_state: list | dict | None = None: initial state (parameter only used if problem is QUBO)

num_reads: int | None = None: number of reads

num_sweeps: int | None = None: number of sweeps

ommx_instance: Instance

ommx.v1.Instance representing a QUBO or HUBO problem

The input instance must be a QUBO (Quadratic Unconstrained Binary Optimization) or HUBO (Higher-order Unconstrained Binary Optimization) problem, i.e.

All decision variables are binary
No constraints
Objective function is quadratic (QUBO) or higher (HUBO).
Minimization problem

You can convert an instance to QUBO or HUBO via ommx.v1.Instance.penalty_method() or other corresponding method.

penalty_weights: dict[int, float]: Penalty weights for each constraint, passed to Instance.to_qubo or Instance.to_hubo

reinitialize_state: bool | None = None: if true reinitialize state for each run (parameter only used if problem is QUBO)

property sampler_input: dict[tuple[int, Ellipsis], float]

schedule: list | None = None: list of inverse temperature (parameter only used if problem is QUBO)

seed: int | None = None: seed for Monte Carlo algorithm

property solver_input: dict[tuple[int, Ellipsis], float]

sparse: bool | None = None: use sparse matrix or not (parameter only used if problem is QUBO)

uniform_penalty_weight: float | None = None: Weight for uniform penalty, passed to Instance.to_qubo or Instance.to_hubo

updater: str | None = None: updater algorithm

decode_to_samples(response: openjij.Response) → Samples: Convert openjij.Response to Samples

response_to_samples(response: openjij.Response) → Samples: Deprecated: renamed to decode_to_samples()

sample_qubo_sa(instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None) → Samples: Deprecated: Use OMMXOpenJijSAAdapter.sample() instead