pygsti.protocols.stability

Stability analysis protocol objects

Module Contents

Classes

StabilityAnalysisDesign

Experimental design for stability analysis.

StabilityAnalysis

Stability Analysis protocol

StabilityAnalysisResults

Results from the stability analysis protocol.

class pygsti.protocols.stability.StabilityAnalysisDesign(circuits, qubit_labels=None)

Bases: pygsti.protocols.protocol.ExperimentDesign

Experimental design for stability analysis.

Parameters
  • circuits (list) – The list of circuits to perform the stability analysis on. These can be anything.

  • qubit_labels (tuple or "multiple", optional) – The qubits that this experiment design applies to. These should also be the line labels of circuits.

class pygsti.protocols.stability.StabilityAnalysis(significance=0.05, transform='auto', marginalize='auto', mergeoutcomes=None, constnumtimes='auto', ids=False, frequencies='auto', freqpointers={}, freqstest=None, tests='auto', inclass_correction={}, betweenclass_weighting='auto', estimator='auto', modelselector=None, verbosity=1, name=None)

Bases: pygsti.protocols.protocol.Protocol

Stability Analysis protocol

Parameters
  • ds (DataSet or MultiDataSet) – A DataSet containing time-series data to be analyzed for signs of instability.

  • significance (float, optional) – The global significance level. With defaults for all other inputs (a wide range of non-default options), the family-wise error rate of the set of all hypothesis tests performed is controlled to this value.

  • transform (str, optional) –

    The type of transform to use in the spectral analysis. Options are:

    • ’auto’: An attempt is made to choose the best transform given the “meta-data” of the data,

      e.g., the variability in the time-step between data points. For beginners, ‘auto’ is the best option. If you are familiar with the underlying methods, the meta-data of the input, and the relative merits of the different transform, then it is probably better to choose this yourself – as the auto-selection is not hugely sophisticated.

    • ’dct’The Type-II Discrete Cosine Transform (with an orthogonal normalization). This is

      the only tested option, and it is our recommended option when the data is approximately equally-spaced, i.e., the time-step between each “click” for each circuit is almost a constant. (the DCT transform implicitly assumes that this time-step is exactly constant)

    • ’dft’The discrete Fourier transform (with an orthogonal normalization). *** This is an

      experimental feature, and the results are unreliable with this transform ***

    • ’lsp’The Lomb-Scargle periodogram. *** This is an experimental feature, and the code is

      untested with this transform ***

  • marginalize (str or bool, optional) – True, False or ‘auto’. Whether or not to marginalize multi-qubit data, to look for instability in the marginalized probability distribution over the two outcomes for each qubit. Cannot be set to True if mergeoutcomes is not None.

  • mergeoutcomes (None or Dict, optional) – If not None, a dictionary of outcome-merging dictionaries. Each dictionary contained as a value of mergeoutcomes is used to create a new DataSet, where the values have been merged according to that dictionary (see the aggregate_dataset_outcomes() function inside datasetconstructions.py). The corresponding key is used as the key for that DataSet, when it is stored in a MultiDataSet, and the instability analysis is implemented on each DataSet. This is a more general data coarse-grainin option than marginalize.

  • constnumtimes (str or bool, optional) – True, False or ‘auto’. If True then data is discarded from the end of the “clickstream” for each circuit until all circuits have the same length clickstream, i.e., the same number of data aquisition times. If ‘auto’ then it is set to True or False depending on the meta-data of the data and the type of transform being used.

  • ids (True or False, optional) – Whether the multiple DataSets should be treat as generated from independent random variables. If the input is a DataSet and marginalize is False and mergeoutcomes is None then this input is irrelevant: there is only ever one DataSet being analyzed. But in general multiple DataSets are concurrently analyzed. This is irrelevant for independent analyses of the DataSets, but the analysis is capable of also implementing a joint analysis of the DataSets. This joint analysis is only valid on the assumption of independent DataSets, and so this analysis will not be permitted unless ids is set to True. Note that the set of N marginalized data from N-qubit circuits are generally not independent – even if the circuits contain no 2-qubit gates then crosstalk can causes dependencies. However, as long as the dependencies are weak then settings this to True is likely ok.

  • frequencies ('auto' or list, optional) –

    The frequencies that the power spectra are calculated for. If ‘auto’ these are automatically determined from the meta-data of the time-series data (e.g., using the mean time between data points) and the transform being used. If not ‘auto’, then a list of lists, where each list is a set of frequencies that are the frequencies corresponding to one or more power spectra. The frequencies that should be paired to a given power spectrum are specified by freqpointers.

    These frequencies (whether automatically calculated or explicitly input) have a fundmentally different meaning depending on whether the transform is time-stamp aware (here, the LSP) or not (here, the DCT and DFT).

    Time-stamp aware transforms take the frequencies to calculate powers at as an input, so the specified frequencies are, explicitly, the frequencies associated with the powers. The task of choosing the frequencies amounts to picking the best set of frequencies at which to interogate the true probability trajectory for components. As there are complex factors involved in this choice that the code has no way of knowing, sometimes it is best to choose them yourself. E.g., if different frequencies are used for different circuits it isn’t possible to (meaningfully) averaging power spectra across circuits, but this might be preferable if the time-step is sufficiently different between different circuits – it depends on your aims.

    For time-stamp unaware transforms, these frequencies should be the frequencies that, given that we’re implementing the, e.g., DCT, the generated power spectrum is implicitly with respect to. In the case of data on a fixed time-grid, i.e., equally spaced data, then there is a precise set of frequencies implicit in the transform (which will be accurately extracted with frequencies set to auto). Otherwise, these frequencies are explicitly at least slightly ad hoc, and choosing these frequencies amounts to choosing those frequencies that “best” approximate the properties being interogatted with fitting each, e.g., DCT basis function to the (timestamp-free) data. The ‘auto’ option bases there frequencies solely on the mean time step and the number of times, and is a decent option when the time stamps are roughly equally spaced for each circuit.

    These frequencies should be in units of 1/t where ‘t’ is the unit of the time stamps.

  • freqpointers (dict, optional) – Specifies which frequencies correspond to which power spectra. The keys are power spectra labels, and the values are integers that point to the index of frequencies (a list of lists) that the relevant frquencies are found at. Whenever a power spectra is not included in freqpointers then this defaults to 0. So if frequencies is specified and is a list containing a single list (of frequencies) then freqpointers can be left as the empty dictionary.

  • freqstest (None or list, optional) – If not not None, a list of the frequency indices at which to test the powers. Leave as None to perform comprehensive testing of the power spectra.

  • tests ('auto' or tuple, optional) –

    Specifies the set of hypothesis tests to perform. If ‘auto’ then an set of tests is automatically chosen. This set of tests will be suitable for most purposes, but sometimes it is useful to override this. If a tuple, the elements are “test classes”, that specifies a set of hypothesis tests to run, and each test class is itself specified by a tuple. The tests specified by each test class in this tuple are all implemented. A test class is a tuple containing some subset of ‘dataset’, ‘circuit’ and ‘outcome’, which specifies a set of power spectra. Specifically, a power spectra has been calculated for the clickstream for every combination of eachinput DataSet (e.g., there are multiple DataSets if there has been marginalization of multi-qubit data), each Circuit in the DataSet, and each possible outcome in the DataSet. For each of “dataset”, “circuit” and “outcome” not included in a tuple defining a test class, the coresponding “axis” of the 3-dimensional array of spectra is averaged over, and these spectra are then tested. So the tuple () specifies the “test class” whereby we test the power spectrum obtained by averaging all power spectra; the tuple (‘dataset’,’circuit’) specifies the “test class” whereby we average only over outcomes, obtaining a single power spectrum for each DataSet and Circuit combination, which we test.

    The default option for “tests” is appropriate for most circumstances, and it consists of (), (‘dataset’) and (‘dataset’, ‘circuit’) with duplicates removed (e.g., if there is a single DataSet then () is equivalent to (‘dataset’)).

  • inclass_correction (dict, optional) – A dictionary with keys ‘dataset’, ‘circuit’, ‘outcome’ and ‘spectrum’, and values that specify the type of multi-test correction used to account for the multiple tests being implemented. This specifies how the statistically significance is maintained within the tests implemented in a single “test class”.

  • betweenclass_weighting ('auto' or dict, optional) – The weighting to use to maintain statistical significance between the different classes of test being implemented. If ‘auto’ then a standard Bonferroni correction is used.

  • estimator (str, optional) –

    The name of the estimator to use. This is the method used to estimate the parameters of a parameterized model for each probability trajectory, after that parameterized model has been selected with the model selection methods. Allowed values are:

    • ’auto’. The estimation method is chosen automatically, default to the fast method that is also

      reasonably reliable.

    • ’filter’. Performs a type of signal filtering: implements the transform used for generating power

      spectra (e.g., the DCT), sets the amplitudes to zero for all freuquencies that the model selection has not included in the model, inverts the transform, and then performs some minor post-processing to guarantee probabilities within [0, 1]. This method is less statically well-founded than ‘mle’, but it is faster and typically gives similar results. This method is not an option for non-invertable transforms, such as the Lomb-Scargle periodogram.

    • ’mle’. Implements maximum likelihood estimation, on the parameterized model chosen by the model

      selection. The most statistically well-founded option, but can be slower than ‘filter’ and relies on numerical optimization.

  • modelselector (tuple, optional) – The model selection method. If not None, a “test class” tuple, specifying which test results to use to decide which frequencies are significant for each circuit, to then construct a parameterized model for each probability trajectory. This can be typically set to None, and it will be chosen automatically. But if you wish to use specific test results for the model selection then this should be set.

  • verbosity (int, optional) – The amount of print-to-screen

  • name (str, optional) – The name of this protocol, also used to (by default) name the results produced by this protocol. If None, the class name will be used.

run(self, data, memlimit=None, comm=None)

Run this protocol on data.

Parameters
  • data (ProtocolData) – The input data.

  • memlimit (int, optional) – A rough per-processor memory limit in bytes.

  • comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator used to run this protocol in parallel.

Returns

StabilityAnalysisResults

class pygsti.protocols.stability.StabilityAnalysisResults(data, protocol_instance, stabilityanalyzer)

Bases: pygsti.protocols.protocol.ProtocolResults

Results from the stability analysis protocol.

NOTE Currently, this object just wraps a pygsti.extras.drift.StabilityAnalyzer object, which historically performed stability analysis. In the future, this object will likely take over the function of StabilityAnalyzer.

Parameters
  • data (ProtocolData) – The experimental data these results are generated from.

  • protocol_instance (Protocol) – The protocol that generated these results.

  • stabilityanalyzer (pygsti.extras.drift.StabilityAnalyzer) – An object holding the stability analysis results. This will likely be updated in the future.

__getattr__(self, attr)