openjij.utils

Submodules

• openjij.utils.benchmark
• openjij.utils.cxx_cast
• openjij.utils.decorator
• openjij.utils.graph_utils
• openjij.utils.res_convertor
• openjij.utils.time_measure

Functions

convert_response(response)
residual_energy(response, ref_energy)	Calculate redisual energy from measure energy
se_lower_tts(tts, success_prob, computation_time, p_r, ...)	param success_prob: success probability.
se_residual_energy(response, ref_energy)	Calculate redisual energy's standard error from measure energy
se_success_probability(response, solutions[, ...])	Calculate success probability's standard error from openjij.response
se_upper_tts(tts, success_prob, computation_time, p_r, ...)	param success_prob: success probability.
solver_benchmark(solver, time_list[, solutions, args, ...])	Calculate 'success probability', 'TTS', 'Residual energy','Standard Error' with computation time
success_probability(response, solutions[, ref_energy, ...])	Calculate success probability from openjij.response
time_to_solution(success_prob, computation_time, p_r)	param success_prob: success probability.

Package Contents

openjij.utils.convert_response(response)

openjij.utils.residual_energy(response, ref_energy)

Calculate redisual energy from measure energy

Parameters:

• response (openjij.Response) – response from solver (or sampler).

• ref_energy (float) – the reference energy (usually use the ground energy)

Returns:

Residual energy which is defined as $\langle E \rangle - E_0$ ($\langle...\rangle$ represents average, $E_0$ is the reference energy (usually use the ground energy)).

Return type:

float

openjij.utils.se_lower_tts(tts, success_prob, computation_time, p_r, se_success_prob)

Parameters:

• success_prob (float) – success probability.

• computation_time (float)

• p_r (float) – thereshold probability to calculate time to solution.

Returns:

time to solution $\tau * \log(1-pr)/\log(1-ps)$ ‘s standard error which pr is thereshold probability, ps is success probability and $tau$ is computation time.

Return type:

float

openjij.utils.se_residual_energy(response, ref_energy)

Calculate redisual energy’s standard error from measure energy

Parameters:

• response (openjij.Response) – response from solver (or sampler).

• ref_energy (float) – the reference energy (usually use the ground energy)

Returns:

redisual energy’s standard error from measure energy

Return type:

float

openjij.utils.se_success_probability(response, solutions, ref_energy=0, measure_with_energy=False)

Calculate success probability’s standard error from openjij.response

Parameters:

• response (openjij.Response) – response from solver (or sampler).

• solutions (list[int]) – true solutions.

Returns:

Success probability’s standard error.

• When measure_with_energy is False, success is defined as getting the same state as solutions.

• When measure_with_energy is True, success is defined as getting a state which energy is below reference energy

Return type:

float

openjij.utils.se_upper_tts(tts, success_prob, computation_time, p_r, se_success_prob)

Parameters:

• success_prob (float) – success probability.

• computation_time (float)

• p_r (float) – thereshold probability to calculate time to solution.

Returens:

float: time to solution $\tau * \log(1-pr)/\log(1-ps)$ ‘s standard error which pr is thereshold probability, ps is success probability and $tau$ is computation time.

openjij.utils.solver_benchmark(solver, time_list, solutions=[], args={}, p_r=0.99, ref_energy=0, measure_with_energy=False, time_name='execution_time')

Calculate ‘success probability’, ‘TTS’, ‘Residual energy’,’Standard Error’ with computation time

Parameters:

• solver (callable) – returns openjij.Response, and solver has arguments ‘time’ and ‘**args’

• time_list (list)

• solutions (list(list(int)), list(int)) – true solution or list of solution (if solutions are degenerated).

• args (dict) – Arguments for solver.

• p_r (float) – Thereshold probability for time to solutions.

• ref_energy (float) – The ground (reference to calculate success probability and the residual energy) energy.

• measure_with_energy (bool) – use a energy as measure for success

Returns:

dictionary which has the following keys:

• time: list of compuation time

• success_prob list of success probability at each computation time

• tts: list of time to solusion at each computation time

• residual_energy: list of residual energy at each computation time

• se_lower_tts: list of tts’s lower standard error at each computation time

• se_upper_tts: list of tts’s upper standard error at each computation time

• se_success_prob: list of success probability’s standard error at each computation time

• se_residual_energy: list of residual_energy’s standard error at each computation time

• info (dict): Parameter information for the benchmark

Return type:

dict

openjij.utils.success_probability(response, solutions, ref_energy=0, measure_with_energy=False)

Calculate success probability from openjij.response

Parameters:

• response (openjij.Response) – response from solver (or sampler).

• solutions (list[int]) – true solutions.

Returns:

Success probability.

• When measure_with_energy is False, success is defined as getting the same state as solutions.

• When measure_with_energy is True, success is defined as getting a state which energy is below reference energy

Return type:

float

openjij.utils.time_to_solution(success_prob, computation_time, p_r)

Parameters:

• success_prob (float) – success probability.

• computation_time (float)

• p_r (float) – thereshold probability to calculate time to solution.

Returns:

time to solution $\tau * \log(1-pr)/\log(1-ps)$ which pr is thereshold probability, ps is success probability and $tau$ is computation time.

Return type:

float