Running a threshold simulation

qsurface.threshold.run_many(Code, Decoder, iterations=1, sizes=[], enabled_errors=[], error_rates=[], faulty_measurements=False, methods_to_benchmark={}, output='', mp_processes=1, recursion_limit=100000, **kwargs)

Runs a series of simulations of varying sizes and error rates.

A series of simulations are run without plotting for all combinations of sizes and error_rates. The results are returned as a Pandas DataFrame and saved to the working directory as a csv file. If an existing csv file with the same file name is found, the existing file is loaded and new results are appended to the existing data. A main.BenchmarkDecoder object is attached to each simulation to log the seed and other information.

Parameters

  • Code (Union[module, str]) – Any surface code module or module name from codes.

  • Decoder (Union[module, str]) – Any decoder module or module name from decoders

  • iterations (int) – Number of iterations to run per configuration.

  • sizes (List[Union[int, Tuple[int, int]]]) – The sizes of the surface configurations.

  • enabled_errors (List[Union[str, Sim]]) – List of error modules from errors.

  • error_rates (List[Dict]) – List of dictionaries for error rates per configuration (see errors).

  • faulty_measurements (bool) – Enable faulty measurements (decode in a 3D lattice).

  • methods_to_benchmark (dict) – Decoder class methods to benchmark.

  • output (str) – File name of outputted csv data. If set to “none”, no file will be saved.

  • mp_processses – Number of processes to spawn. For a single process, run is used. For multiple processes, run_multiprocess is utilized.

Examples

A series of simulations using the toric surface code and mwpm decoder can be easily setup. Benchmarking can be performed by supplying the methods_to_benchmark argument of the BenchmarkDecoder class. The function will initialize a benchmark object of each configuration and append all results as columns to the returned dataframe.

>>> data = run_many(
...     "toric",
...     "mwpm",
...     iterations = 1000,
...     sizes = [8,12,16],
...     enabled_errors = ["pauli"],
...     error_rates = [{"p_bitflip: p} for p in [0.09, 0.1, 0.11]],
... )
>>> print(data)
                datetime  decoded    iterations  no_error  p_bitflip          seed  size
0   04/11/2020 14:45:36   1000.0        1000.0     820.0       0.09  13163.013981   8.0
1   04/11/2020 14:45:45   1000.0        1000.0     743.0       0.10  13172.277886   8.0
2   04/11/2020 14:45:54   1000.0        1000.0     673.0       0.11  13181.090130   8.0
3   04/11/2020 14:46:36   1000.0        1000.0     812.0       0.09  13190.191461  12.0
4   04/11/2020 14:47:18   1000.0        1000.0     768.0       0.10  13232.408302  12.0
5   04/11/2020 14:48:16   1000.0        1000.0     629.0       0.11  13274.044268  12.0
6   04/11/2020 14:51:47   1000.0        1000.0     855.0       0.09  13332.153639  16.0
7   04/11/2020 14:55:15   1000.0        1000.0     754.0       0.10  13542.533067  16.0
8   04/11/2020 14:59:14   1000.0        1000.0     621.0       0.11  13751.082511  16.0
Return type

Optional[DataFrame]

qsurface.threshold.read_csv(file)

Reads a CSV file parses it as a Pandas DataFrame.

Return type

DataFrame

class qsurface.threshold.ThresholdFit(modified_ansatz=False, p=0.09, 0.1, 0.11, A=- inf, 0, inf, B=- inf, 0, inf, C=- inf, 0, inf, D=- 2, 1.6, 2, nu=0.8, 0.95, 1.2, mu=0, 0.7, 3)

Fitter for code threshold with data obtained by ~.threshold.run.

Threshold fitting is performed using the equations described in [wang2003confinement]. The threshold is computing the ground state of the Hamiltonian that described the phase transition or the Nishimori line in the Random Bond Ising Model. The source provides two functions which are included in this fitting class, where the modified_ansatz includes a nonuniversion additive correction to correct for finite size effects.

wang2003confinement

Chenyang Wang, Jim Harrington and John Preskill, Confinement-Higgs transition in a disordered gauge theory and the accuracy threshold for quantum memory, Annals of Physics, 1:31-58, 2003.

fit_data(data, column, **kwargs)

Fits for the code threshold.

Parameters

  • data (DataFrame) – Data obtained via run.

  • column (str) – The column of the DataFrame to fit for.

  • kwargs – Keyword arguments are passed on the scipy.curve_fit.

plot_data(data, column, figure=None, rescaled=False, scatter_kwargs={'s': 10}, line_kwargs={'alpha': 0.5, 'ls': 'dashed', 'lw': 1.5}, axis_attributes={'title': 'Threshold', 'xlabel': 'Physical error rate', 'ylabel': 'Logical error rate'}, num_x_fit=1000, **kwargs)

Plots the inputted data and the fit for the code threshold.

Parameters

  • data (DataFrame) – Data obtained via run.

  • column (str) – The column of the DataFrame to fit for.

  • figure (Optional[Figure]) – If a figure is attached, show is not called. Instead, the figure is returned for futher manipulation.

  • rescaled (bool) – Plots the data on a rescaled x axis where the fit is a single line.

  • scatter_kwargs (dict) – Keyword arguments to pass on to the scatter for the markers.

  • line_kwargs (dict) – Keyword arguments to pass on to the ~matplotlib.pyplot.plot for the line plot.

  • axis_attributes (dict) – Attributes to set of the axis via axis.set_{attribute}(value).

  • num_x_fit (int) – Numpy of points to plot for the fit.