Simulation

class panqec.simulation.BaseSimulation(code: StabilizerCode, error_model: BaseErrorModel, compress: bool = True, verbose=True, rng=None)

Quantum Error Correction Simulation.

__init__(code: StabilizerCode, error_model: BaseErrorModel, compress: bool = True, verbose=True, rng=None)

load_results(output_file: str): Load previously written results from directory.

save_results(output_file: str): Save results to directory.

class panqec.simulation.BatchSimulation(output_file: str, label='unlabelled', on_update: ~typing.Callable = <function identity>, update_frequency: int = 5, save_frequency: int = 1, method: str = 'direct', log_file: str | None = None, verbose: bool = True)

Holds and controls many simulations.

Parameters:

label (str) – The label of the files.
output_file (str) – Path to the file (.json or .json.gz) that will store the simulation results
label – Label of the batch simulation
on_update (Callable) – Function that gets called on every update.
update_frequency (int) – Frequency at which to update results.
save_frequency (int) – Frequency at which to write results to file on disk.
method (str) – The method can be either “direct” or “splitting”. The direct method samples independent errors at each iteration, while the splitting method (by Bravyi & Vargo) uses MCMC to determine the next error to sample. Ref: arXiv:1308.6270

__init__(output_file: str, label='unlabelled', on_update: ~typing.Callable = <function identity>, update_frequency: int = 5, save_frequency: int = 1, method: str = 'direct', log_file: str | None = None, verbose: bool = True)

append(simulation: BaseSimulation)

Append a simulation to the current BatchSimulation.

Parameters:: simulation (BaseSimulation) – The simulation to append.

estimate_remaining_time(n_trials: int)

Estimate remaining time given target n_trials.

Parameters:: n_trials (int) – The target total number of trials.

load_results(): Load results from disk.

on_update(n_trials: int): Function that gets called on every update. It uses the total number of runs, n_trials, to estimate the remaining time

run(n_trials, progress: ~typing.Callable = <function identity>)

Perform the running.

Parameters:

n_trials (int) – Number of trials to run.
progress (Callable) – The progress bar, such as tqdm.

save_file(): Do a complete save of the file.

property wall_time: Total time run so far.

class panqec.simulation.DirectSimulation(code: StabilizerCode, error_model: BaseErrorModel, decoder: BaseDecoder, error_rate: float, compress: bool = True, verbose=True, rng=None)

Quantum Error Correction Simulation.

Parameters:

code (StabilizerCode) – The code to simulate.
error_model (BaseErrorModel) – The error model to use.
decoder (BaseDecoder) – The decoder to use.
error_rate (float) – The error rate parameter.
compress (bool) – Set False to not compress the output files and save as plain json.
verbose (bool) – Set False to suppress output.
rng – Set Random number generator if you want to seed it.

__init__(code: StabilizerCode, error_model: BaseErrorModel, decoder: BaseDecoder, error_rate: float, compress: bool = True, verbose=True, rng=None)

get_results(): Return results as dictionary.

class panqec.simulation.SplittingSimulation(code: StabilizerCode, error_model: BaseErrorModel, decoders: List[BaseDecoder], error_rates: List[float], n_init_runs: int, start_run: int = 0, compress: bool = True, verbose: bool = True, rng=None)

Quantum Error Correction Simulation.

__init__(code: StabilizerCode, error_model: BaseErrorModel, decoders: List[BaseDecoder], error_rates: List[float], n_init_runs: int, start_run: int = 0, compress: bool = True, verbose: bool = True, rng=None)

get_results(): Return results as dictionary.

panqec.simulation.read_input_dict(data: dict, output_file: str, verbose: bool = True, *args, **kwargs) → BatchSimulation

Return BatchSimulation from input dict.

Parameters:: data (dict) – Data that has been parsed from an input json file.
Returns:: batch_simulation – BatchSimulation object populated with the simulations specified in the input data.
Return type:: BatchSimulation

panqec.simulation.read_input_json(input_file: str, output_file: str, log_file: str | None = None) → BatchSimulation: Read json input file or .json.gz file.

panqec.simulation.run_file(input_file: str, output_file: str, n_trials: int, progress: ~typing.Callable = <function identity>, log_file: str | None = None, verbose: bool = True)

Run an input json file.

Parameters:

input_file (str) – Path to the input json file.
output_file (str) – Path to the json file that will contain the results.
n_trials (int) – The number of MCMC trials to do.
progress (Callable) – Callable function
verbose (bool,) – Verbosity of the output

Return type:

None

panqec.simulation.run_once(code: StabilizerCode, error_model: BaseErrorModel, decoder: BaseDecoder, error_rate: float, rng=None) → dict: Run a simulation once and return the results as a dictionary.