ommx_openjij_adapter

Classes

OMMXOpenJijSAAdapter

Sampling QUBO 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 ommx_openjij_adapter.OMMXOpenJijSAAdapter(ommx_instance: ommx.v1.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 with Simulated Annealing (SA) by openjij.SASampler

decode(data: SamplerOutput) → ommx.v1.Solution

decode_to_samples(data: openjij.Response) → ommx.v1.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) → ommx.v1.SampleSet

classmethod sample(ommx_instance: ommx.v1.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) → ommx.v1.SampleSet

classmethod solve(ommx_instance: ommx.v1.Instance, **kwargs) → ommx.v1.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

initial_state: list | dict | None = None: initial state

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

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

ommx_instance: ommx.v1.Instance

ommx.v1.Instance representing a QUBO problem

The input instance must be a QUBO (Quadratic Unconstrained Binary Optimization) problem, i.e.

Every decision variables are binary
No constraint
Objective function is quadratic
Minimization problem

You can convert an instance to QUBO 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

reinitialize_state: bool | None = None: if true reinitialize state for each run

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

schedule: list | None = None: list of inverse temperature

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

property solver_input: SamplerInput

sparse: bool | None = None: use sparse matrix or not

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

updater: str | None = None: updater algorithm

ommx_openjij_adapter.decode_to_samples(response: openjij.Response) → ommx.v1.Samples: Convert openjij.Response to Samples

ommx_openjij_adapter.response_to_samples(response: openjij.Response) → ommx.v1.Samples: Deprecated: renamed to decode_to_samples()

ommx_openjij_adapter.sample_qubo_sa(instance: ommx.v1.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) → ommx.v1.Samples: Deprecated: Use OMMXOpenJijSAAdapter.sample() instead