pygsti.protocols.stability
Stability analysis protocol objects
Module Contents
Classes
Experimental design for stability analysis. 

Stability Analysis protocol 

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
 circuitslist
The list of circuits to perform the stability analysis on. These can be anything.
 qubit_labelstuple or “multiple”, optional
The qubits that this experiment design applies to. These should also be the line labels of circuits.
Create a new ExperimentDesign object, which holds a set of circuits (needing data).
Parameters
 circuitslist of Circuits, optional
A list of the circuits needing data. If None, then the list is empty.
 qubit_labelstuple or “multiple”, optional
The qubits that this experiment design applies to. These should also be the line labels of circuits. If None, the concatenation of the qubit labels of any child experiment designs is used, or, if there are no child designs, the line labels of the first circuit is used. The special “multiple” value means that different circuits act on different qubit lines.
 childrendict, optional
A dictionary of whose values are child
ExperimentDesign
objects and whose keys are the names used to identify them in a “path”. children_dirsdict, optional
A dictionary whose values are directory names and keys are child names (the same as the keys of children). If None, then the keys of children must be strings and are used as directory names. Directory names are used when saving the object (via
write()
).
Returns
ExperimentDesign
 class pygsti.protocols.stability.StabilityAnalysis(significance=0.05, transform='auto', marginalize='auto', mergeoutcomes=None, constnumtimes='auto', ids=False, frequencies='auto', freqpointers=None, freqstest=None, tests='auto', inclass_correction=None, betweenclass_weighting='auto', estimator='auto', modelselector=None, verbosity=1, name=None)
Bases:
pygsti.protocols.protocol.Protocol
Stability Analysis protocol
Parameters
 dsDataSet or MultiDataSet
A DataSet containing timeseries data to be analyzed for signs of instability.
 significancefloat, optional
The global significance level. With defaults for all other inputs (a wide range of nondefault options), the familywise error rate of the set of all hypothesis tests performed is controlled to this value.
 transformstr, 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 “metadata” of the data,
e.g., the variability in the timestep between data points. For beginners, ‘auto’ is the best option. If you are familiar with the underlying methods, the metadata of the input, and the relative merits of the different transform, then it is probably better to choose this yourself – as the autoselection is not hugely sophisticated.
 ‘dct’The TypeII Discrete Cosine Transform (with an orthogonal normalization). This is
the only tested option, and it is our recommended option when the data is approximately equallyspaced, i.e., the timestep between each “click” for each circuit is almost a constant. (the DCT transform implicitly assumes that this timestep 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 LombScargle periodogram.
This is an experimental feature, and the code is untested with this transform
 marginalizestr or bool, optional
True, False or ‘auto’. Whether or not to marginalize multiqubit 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.
 mergeoutcomesNone or Dict, optional
If not None, a dictionary of outcomemerging 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 coarsegrainin option than marginalize.
 constnumtimesstr 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 metadata 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 Nqubit circuits are generally not independent – even if the circuits contain no 2qubit 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 metadata of the timeseries 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 timestamp aware (here, the LSP) or not (here, the DCT and DFT).
Timestamp 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 timestep is sufficiently different between different circuits – it depends on your aims.
For timestamp 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 timegrid, 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 (timestampfree) 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.
 freqpointersdict, 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.
 freqstestNone 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 multiqubit 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 3dimensional 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_correctiondict, optional
A dictionary with keys ‘dataset’, ‘circuit’, ‘outcome’ and ‘spectrum’, and values that specify the type of multitest 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.
 estimatorstr, 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 postprocessing to guarantee probabilities within [0, 1]. This method is less statically wellfounded than ‘mle’, but it is faster and typically gives similar results. This method is not an option for noninvertable transforms, such as the LombScargle periodogram.
 ‘mle’. Implements maximum likelihood estimation, on the parameterized model chosen by the model
selection. The most statistically wellfounded option, but can be slower than ‘filter’ and relies on numerical optimization.
 modelselectortuple, 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.
 verbosityint, optional
The amount of printtoscreen
 namestr, 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.
Implements instability (“drift”) detection and characterization on timeseries data from any set of quantum circuits on any number of qubits. This uses the StabilityAnalyzer object, and directly accessing that object allows for some more complex analyzes to be performed. That object also offers a more stepbystep analysis procedure, which may be helpful for exploring the optional arguments of this analysis.
Parameters
 dsDataSet or MultiDataSet
A DataSet containing timeseries data to be analyzed for signs of instability.
 significancefloat, optional
The global significance level. With defaults for all other inputs (a wide range of nondefault options), the familywise error rate of the set of all hypothesis tests performed is controlled to this value.
 transformstr, 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 “metadata” of the data,
e.g., the variability in the timestep between data points. For beginners, ‘auto’ is the best option. If you are familiar with the underlying methods, the metadata of the input, and the relative merits of the different transform, then it is probably better to choose this yourself – as the autoselection is not hugely sophisticated.
 ‘dct’The TypeII Discrete Cosine Transform (with an orthogonal normalization). This is
the only tested option, and it is our recommended option when the data is approximately equallyspaced, i.e., the timestep between each “click” for each circuit is almost a constant. (the DCT transform implicitly assumes that this timestep 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 LombScargle periodogram.
This is an experimental feature, and the code is untested with this transform
 marginalizestr or bool, optional
True, False or ‘auto’. Whether or not to marginalize multiqubit 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.
 mergeoutcomesNone or Dict, optional
If not None, a dictionary of outcomemerging 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 coarsegrainin option than marginalize.
 constnumtimesstr 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 metadata 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 Nqubit circuits are generally not independent – even if the circuits contain no 2qubit 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 metadata of the timeseries 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 timestamp aware (here, the LSP) or not (here, the DCT and DFT).
Timestamp 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 timestep is sufficiently different between different circuits – it depends on your aims.
For timestamp 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 timegrid, 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 (timestampfree) 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.
 freqpointersdict, 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.
 freqstestNone 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 multiqubit 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 3dimensional 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_correctiondict, optional
A dictionary with keys ‘dataset’, ‘circuit’, ‘outcome’ and ‘spectrum’, and values that specify the type of multitest 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.
 estimatorstr, 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 postprocessing to guarantee probabilities within [0, 1]. This method is less statically wellfounded than ‘mle’, but it is faster and typically gives similar results. This method is not an option for noninvertable transforms, such as the LombScargle periodogram.
 ‘mle’. Implements maximum likelihood estimation, on the parameterized model chosen by the model
selection. The most statistically wellfounded option, but can be slower than ‘filter’ and relies on numerical optimization.
 modelselectortuple, 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.
 verbosityint, optional
The amount of printtoscreen
Returns
StabilityAnalysis
 run(data, memlimit=None, comm=None)
Run this protocol on data.
Parameters
 dataProtocolData
The input data.
 memlimitint, optional
A rough perprocessor memory limit in bytes.
 commmpi4py.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
 dataProtocolData
The experimental data these results are generated from.
 protocol_instanceProtocol
The protocol that generated these results.
 stabilityanalyzerpygsti.extras.drift.StabilityAnalyzer
An object holding the stability analysis results. This will likely be updated in the future.
Initialize an empty Results object.