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)

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) → ommx.v1.SampleSet

classmethod solve(ommx_instance: ommx.v1.Instance) → ommx.v1.Solution

beta_max: float | None = None

maximum value of inverse temperature

beta_min: float | None = None

minimal value of inverse temperature

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.

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

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