Estimate

trueq.estimate.base.Estimate

Base class for all estimate types, which store fit information returned from data analysis.

trueq.estimate.base.EstimateTuple

A namedtuple container for storing a single estimate name, value, and standard deviation.

trueq.estimate.base.EstimatesTuple

A namedtuple container for storing a 1-D array of values and standard deviations for a particular parameter name.

trueq.estimate.EstimateCollection

An iterable container of Estimates with several convenience functions which make it easier to select specific estimates.

trueq.estimate.ArrayAxis

Contains a name and indices for a single axis of an EstimateArray.

trueq.estimate.EstimateArray

Stores a multi-dimensional array of estimate values (and corresponding standard deviations in a separate array) for a specified parameter name.

trueq.estimate.NormalEstimate

Stores a joint estimate of a collection of system parameters.

trueq.estimate.RCalEstimate

Stores estimates for the confusion matrices associated with readout errors.

trueq.estimate.comp_table.CompTable

Organizes NormalEstimates into a parent-children structure based on twirling groups in a way that makes it easier to compare values.

trueq.estimate.knr.KnrBodyEstimate

Represents a Pauli probability distribution for some subset of qubits of a cycle of interest, as measured by KNR; see also make_knr().

trueq.estimate.knr.KnrDataTable

Organizes KNR estimates into a table with columns grouped by cycle (and possibly other keywords, see group_by) and rows grouped by error modes.

Estimate (Parent Class)

class trueq.estimate.base.EstimateTuple(name, val, std)

A namedtuple container for storing a single estimate name, value, and standard deviation.

Parameters
  • name (str) – The name of the estimate.

  • val (float | complex) – The value of the estimate.

  • std (float) – The standard deviation of the estimate.

class trueq.estimate.base.Estimate(key, options=None)

Base class for all estimate types, which store fit information returned from data analysis.

Inheritance diagram of trueq.estimate.NormalEstimate, trueq.estimate.RCalEstimate, trueq.estimate.KnrBodyEstimate

This class is not expected to be used directly, and fitting results will return subclasses such as NormalEstimate or RCalEstimate.

Parameters
  • key (Key) – A hashable object used for record keeping in analysis. For example, Key(protocol='SRB', n_random_cycles=10).

  • options (NoneType | dict) – Options that were passed to fit() method.

to_dict()

Converts the estimate to a dictionary representation.

Note

The stored Key is also converted to a dictionary.

Return type

dict

static from_dict(dic)

Converts a dictionary representation into an estimate instance.

Note

See to_dict() for more details.

Parameters

dic (dict) – The dictionary representation of the estimate.

Returns

An Estimate or one of its subclasses depending on the dic that is provided.

Return type

Estimate-like

property key

The Key of this estimate, used for bookkeeping.

Type

Key

property options

The fit options that were used to create this estimate.

Type

dict

Estimate Collection

class trueq.estimate.base.EstimatesTuple(name, vals, stds, sweeps)

A namedtuple container for storing a 1-D array of values and standard deviations for a particular parameter name.

Parameters
  • name (str) – The name of the estimates.

  • vals (np.ndarray) – An array of estimates.

  • stds (np.ndarray) – The corresponding standard deviations of the estimates.

  • sweeps (dict) – A dictionary mapping keyword names to lists of keyword values, where each list has the same length as vals.

class trueq.estimate.EstimateCollection(estimates=None)

An iterable container of Estimates with several convenience functions which make it easier to select specific estimates.

import trueq as tq

circuits = tq.make_srb([[0], [1, 2]], [4, 32])
circuits += tq.make_xrb([[0], [1, 2]], [4, 32])
tq.Simulator().add_overrotation(0.04).run(circuits)

estimate_collection = circuits.fit()
estimate_collection
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SRB
Streamlined Randomized Benchmarking
Cliffords
(0,)
Key:
  • labels: (0,)
  • protocol: SRB
  • twirl: Cliffords on [0, (1, 2)]
Cliffords
(1, 2)
Key:
  • labels: (1, 2)
  • protocol: SRB
  • twirl: Cliffords on [0, (1, 2)]
${e}_{F}$
The probability of an error acting on the targeted systems during a random gate.
1.6e-03 (3.9e-04)
0.0015613158991331366, 0.00038832847831533397
0.0e+00 (0.0e+00)
0.0, 0.0
${p}$
Decay parameter of the exponential decay $Ap^m$.
1.0e+00 (5.2e-04)
0.9979182454678225, 0.0005177713044204452
1.0e+00 (0.0e+00)
1.0, 0.0
${A}$
SPAM parameter of the exponential decay $Ap^m$.
1.0e+00 (4.9e-03)
0.9962700898067363, 0.00492247719255744
1.0e+00 (0.0e+00)
1.0, 0.0
XRB
Extended Randomized Benchmarking
Cliffords
(0,)
Key:
  • labels: (0,)
  • protocol: XRB
  • twirl: Cliffords on [0, (1, 2)]
Cliffords
(1, 2)
Key:
  • labels: (1, 2)
  • protocol: XRB
  • twirl: Cliffords on [0, (1, 2)]
${e}_{U}$
The process infidelity of the coherent error acting on the specifed systems during a random gate.
1.5e-03 (5.1e-04)
0.0014955131383900533, 0.0005068217716697481
-1.8e-04 (1.2e-04)
-0.00018035790120107809, 0.00012440033570080948
${e}_{S}$
The probability of a stochastic error acting on the specified systems during a random gate.
6.6e-05 (3.3e-04)
6.580276074308333e-05, 0.0003256828229547262
1.8e-04 (1.2e-04)
0.00018035790120107809, 0.00012440033570080948
${u}$
The unitarity of the noise, that is, the average decrease in the purity of an initial state.
1.0e+00 (8.7e-04)
0.9998245317446894, 0.0008684303790022645
1.0e+00 (2.7e-04)
0.9996152711750085, 0.0002653395181170214
${A}$
SPAM parameter of the exponential decay $Au^m$.
1.0e+00 (1.1e-02)
1.025376657018836, 0.010834575026676901
1.1e+00 (6.5e-03)
1.0670198712625796, 0.006513662414646076
Parameters

estimates (NoneType | Estimate-like | Iterable) – Either a single Estimate or an iterable of several estimates. If None is provided, the EstimateCollection will be empty.

append(estimate)

Appends a single Estimate or an iterable of several estimates to the collection.

Parameters

estimate (Estimate-like | Iterable) – Either a single Estimate or an iterable of several estimates.

Returns

This estimate collection.

Return type

EstimateCollection

keys(**filter)

Returns the set of all keys of this estimate collection matching the given filter.

import trueq as tq

circuits = tq.make_srb([[0], [1, 2]], [4, 32])
circuits += tq.make_xrb([[0], [1, 2]], [4, 32])
tq.Simulator().add_overrotation(0.04).run(circuits)

estimate_collection = circuits.fit()
estimate_collection.keys(protocol="SRB")
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KeySet
List of all the keys in the KeySet
protocol
The characterization protocol used to generate a circuit.
labels twirl
The twirling group used to generate a circuit.
Key
Key:
  • labels: (0,)
  • protocol: SRB
  • twirl: Cliffords on [0, (1, 2)]
SRB (0,) Cliffords on [0, (1, 2)]
Key
Key:
  • labels: (1, 2)
  • protocol: SRB
  • twirl: Cliffords on [0, (1, 2)]
SRB (1, 2) Cliffords on [0, (1, 2)]
Parameters

**filter – The filter on keys.

Return type

KeySet

subset(name_pattern=None, filter_fn=None, **filter)

Returns an EstimateCollection containing only the estimates in this collection that match the filter on keys, and whose parameter names match the given filter function or the pattern.

Parameters
  • name_pattern (string) – A regular expression that specifies a pattern for the names, for example "(p_ZZ.*)|(e_F)" to match any name that starts with p_ZZ or any name equal to e_F.

  • filter_fn (function) – A function which accepts an Estimate and returns either True or False.

  • **filter – The filter on keys.

Return type

EstimateCollection

one_or_none(filter_fn=None, **filter)

Returns a single estimate if it is the only estimate in this collection that matches the filter. Otherwise returns None.

Parameters
  • filter_fn (function) – A function which accepts an Estimate and returns either True or False

  • **filter – The filter on keys.

Return type

Estimate-like | NoneType

sorted(*names)

Returns an EstimateCollection sorted by the keys contained in the estimates, see trueq.KeySet.sorted() for more information.

Parameters

*names – One or more name strings that should take priority in sorting.

Return type

EstimateCollection

update_keys(*other, keep=None, remove=None, **kwargs)

Updates every estimate’s key in this collection with new keywords/values. If a given key does not have a given keyword, it is added. If it already exists, it is overwritten. See also trueq.Key.copy() which this method uses.

import trueq as tq

# generate an estimate collection by calling fit() on some circuits
circuits = tq.make_srb([[0], [1, 2]], [4, 32])
circuits += tq.make_xrb([[0], [1, 2]], [4, 32])
tq.Simulator().add_overrotation(0.04).run(circuits)
estimates = circuits.fit()

# give each circuit a new keyword 'banana' with value 10
estimates.update_keys(banana=10)

estimates.keys()
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KeySet
List of all the keys in the KeySet
protocol
The characterization protocol used to generate a circuit.
labels twirl
The twirling group used to generate a circuit.
banana
Key
Key:
  • banana: 10
  • labels: (0,)
  • protocol: SRB
  • twirl: Cliffords on [0, (1, 2)]
SRB (0,) Cliffords on [0, (1, 2)] 10
Key
Key:
  • banana: 10
  • labels: (1, 2)
  • protocol: SRB
  • twirl: Cliffords on [0, (1, 2)]
SRB (1, 2) Cliffords on [0, (1, 2)] 10
Key
Key:
  • banana: 10
  • labels: (0,)
  • protocol: XRB
  • twirl: Cliffords on [0, (1, 2)]
XRB (0,) Cliffords on [0, (1, 2)] 10
Key
Key:
  • banana: 10
  • labels: (1, 2)
  • protocol: XRB
  • twirl: Cliffords on [0, (1, 2)]
XRB (1, 2) Cliffords on [0, (1, 2)] 10
Parameters
  • *other – One or more dict-like objects (e.g. Key or dict) to update the keys with. Updating is applied in the given order. If a name specified in any of these objects already exists after the keep or remove process has taken place, it is updated.

  • keep (str | list) – A string or list of strings specifying the only names to keep during the updates. By default, all names are kept. Only one of the options keep or remove may be used.

  • remove (str | list) – A string or list of strings specifying names to remove during the updates. By default, no names are removed. Only one of the options keep or remove may be used.

  • **kwargs – Name-value items to update the keys with. If a name specified here already exists after the keep or remove process has taken place, it is updated.

Returns

This estimate collection.

Return type

EstimateCollection

Raises

ValueError – If the mutally exclusive keep and remove are both set to True.

array(name, *axes, name_axis=0)

Constructs a multi-dimensional array of estimate values for a specified parameter name. Each axis corresponds to a sweep over metadata values from a particular Key name from the estimates in this collection, or over parameter names themselves if name is provided as a pattern with more than one match.

import trueq as tq

circuits = tq.make_srb([0, 1, 2, 3, 4], [4, 32])
circuits += tq.make_xrb([0, 1, 2, 3, 4], [4, 32])
tq.Simulator().add_overrotation(0.04).run(circuits)
fit = circuits.fit()

# extract a 2-d array of 'p' estimates with first axis over qubit labels,
# and second axis over protocol
arr = fit.array("p", "labels", "protocol")

# we can look at the axes and value array directly
print(arr.axes)
print(arr.vals, "\n")

# we can also look at the html representation, which condenses all but the
# last axis into rows, and displays the last axis as columns
arr
(ArrayAxis('labels', ((0,), (1,), (2,), (3,), (4,))), ArrayAxis('protocol', ('SRB',)))
[[0.9982468 ]
 [0.99672371]
 [0.99640412]
 [0.9953526 ]
 [0.99673775]] 

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protocol SRB
labels
(0,) 9.98e-01 (4.97e-04)
val=0.998247
std=0.000496923
(1,) 9.97e-01 (1.01e-03)
val=0.996724
std=0.00101182
(2,) 9.96e-01 (8.70e-04)
val=0.996404
std=0.000870105
(3,) 9.95e-01 (1.26e-03)
val=0.995353
std=0.00126453
(4,) 9.97e-01 (8.80e-04)
val=0.996738
std=0.000879854

Note

The extracted array is always (hyper-)rectangular, whereas estimate collections are often ragged. When a particular value does not exist in the collection, np.nan is inserted as the array element.

Parameters
  • name (str | ArrayAxis) – The name of the parameter to extract, e.g. "e_F", a regular expression whose matched named are extracted, e.g. "e_F|p" or "e__[IXYZ]+", or an ArrayAxis whose values are parameter names.

  • *axes – A sequence of strings or ArrayAxis that are used to index axes of the array. Each axis (except possibly one defined by name, which is a special case) corresponds to metadata values from a specific keyword found in the keys() of this collection. For example, a string axis value of "protocol" will cause the corresponding axis to be over different protocols sorted by protocol name, or a string axis value of "labels" will be over sorted subsystem labels. For more fine-grained control, axes can be manually specified by ArrayAxis instances.

  • name_axis (int) – In the case where multple names are matched, either because name is a regular expression with multiple matching names or because name was given as an ArrayAxis with multiple values, which axis to put the names on. The default is the 0’th axis, and the remaining axes are ordered according to the order of the axes.

Return type

EstimateArray

Raises
  • ValueError – If no estimates with names matching name are found.

  • ValueError – If multiple estimates with the same name are found that do not differ on their axis values.

values(name, *sort_by, skip_missing=True)

values() was deprecated in version 2.13.0 and will be removed no earlier than version 2.14.0. Use EstimateCollection.array() instead.

Returns all the values of a particular name across Estimates in this collection.

Parameters
  • name (str) – The name of the parameter to extract.

  • *sort_by – One or more key name strings to sort values by, in which case the sort order is lexicographical. If this parameter is not provided, the order of this collection is preserved.

  • skip_missing (bool) – Whether to skip estimates without the given name, or to include numpy.nan values at these locations.

Return type

tuple

to_dict_list()

Returns a list of dictionary representations of the Estimate objects in this collection.

Return type

list

static from_dict_list(dict_list)

Creates a new estimate collection from a list of dictionary representations of Estimate objects.

Parameters

dict_list (list) – A list of dictionary representations of estimate objects.

Return type

EstimateCollection

property plot

An object that stores all plotting functions deemed relevant to this EstimateCollection. If one of these functions is called, the data from this estimate collection is analyzed and used.

Type

PlottingSuite

Estimate Array

class trueq.estimate.ArrayAxis(name, indices, is_name_axis=False)

Contains a name and indices for a single axis of an EstimateArray.

Parameters
  • name (str) – The name of the axis.

  • indices (Iterable) – The indexing values of the axis. These can be of any type. The length should match the dimension of the axis.

  • is_name_axis (bool) – Whether this axis is over parameter names; at most one such axis can exist for an EstimateArray.

property name

The name of this axis.

Type

str

property size

The size of this axis.

Type

int

property indices

The indexing values of this axis.

Type

tuple

property is_name_axis

Whether this axis is over parameter names.

bool

static from_keys(name, keys)

Constructs a new ArrayAxis by collecting all unique values of the given name from the keys and sorting them if possible.

Parameters
  • name (str) – The keyname to collect.

  • keys (KeySet) – Some keys to collect values from.

Return type

ArrayAxis

Raises

KeyError – If the name is not present in the keys.

class trueq.estimate.EstimateArray(vals, stds, axes)

Stores a multi-dimensional array of estimate values (and corresponding standard deviations in a separate array) for a specified parameter name. Each axis corresponds to a sweep over metadata values from a particular Key name from the estimates in an EstimateCollection, or over parameter names themselves. Instances of this class are typically constructed by array().

Parameters
  • vals (array_like) – An array of parameter values estimates.

  • stds (array_like) – The standard deviations of each member of vals, an array with the same shape as vals.

  • axes (Iterable) – A sequence of ArrayAxis instances whose sizes must match the shape of vals and stds.

property axes

A tuple of ArrayAxis instances which describe each axis of this array.

Type

tuple

property dtype

The data type of vals.

Type

type

property ndim

The number of axes owned by vals and stds.

int

property shape

The shape of vals and stds.

tuple

property stds

An array of parameter value standard deviations, with the same shape as vals. The axes are described by axes.

Type

array_like

property vals

An array of parameter values, with the same shape as vals. The axes are described by axes.

Type

array_like

nan_reason(idxs)

Returns a reason for why the array element at the given index is numpy.nan. If it is a number, then an empty string is returned.

Parameters

idxs (tuple) – The nd-index to address.

Return type

str

to_dataframe()

Converts this array to a dataframe.

Return type

pandas.DataFrame

Normal Estimates

class trueq.estimate.NormalEstimate(key, names, values, err=None, raw=None, options=None)

Stores a joint estimate of a collection of system parameters. Each parameter is described by a name and an estimated mean value. These mean values are assumed to be normally distributed, and their covariance matrix (or vector of standard deviations for uncorrelated estimates) is optionally present.

import trueq as tq

estimate = tq.NormalEstimate(
    tq.Key(), ["a", "b", "c"], [1, 2, 3], [0.2, 0.2, 0.2]
)

# get the estimated value of b
estimate.b
EstimateTuple(name='b', val=2, std=0.2)
Parameters
  • key (Key) – A key used for record keeping in analysis. For example, Key(protocol='SRB', n_random_cycles=10).

  • names (Iterable) – An iterable collection of strings which describe the parameters returned from fitting.

  • values (list | numpy.ndarray) – An iterable containing the estimated values associated with the provided names.

  • err (NoneType | numpy.ndarray | list) – Optional array or list containing either the covariance matrix or the standard deviation vector associated with the estimated values. If None is provided then this is set to a vector of zeros.

  • raw (NoneType | dict) – Optional, the data used to estimate the values. If not provided, this defaults to an empty dictionary.

  • options (NoneType | dict) – Options that were passed to fit() method.

Raises

ValueError – If the lengths of names and err (if provided) do not match the length of values.

property names

An iterable collection of strings which describe the parameters returned from fitting.

Type

Iterable

property values

An iterable containing the estimated values associated with the provided names.

Type

Iterable

property raw

The data used to estimate the values. The format of this dictionary may vary depending on who constructed it. Some examples of the format used by True-Q™ protocols are:

Protocols

Format

IRB, SRB, XRB

{<sequence_length>: [<circuit expectation values>], ...}

CB, KNR, SC

{<measurement_basis>: {<sequence_length>: [<circuit expectation values>], ...}, ...}

Type

dict

property decays

decays() was deprecated in version 2.13.0 and will be removed no earlier than version 2.14.0. This attribute is now called ‘raw’.

Deprecated; use raw instead.

Type

dict

property err

Array containing either the covariance matrix or the standard deviation vector associated with the estimated values. If err=None was provided, this will be a vector of zeros.

Type

numpy.ndarray

property cov

The covariance matrix of this estimate.

If err=None was provided, this will be an array of zeros.

Type

numpy.ndarray

property std

A vector of standard deviations for every element of this estimate.

If err=None was provided, this will be a vector of zeros.

If a covariance matrix is present then this is the square root of diagonal terms.

Type

numpy.ndarray

subset(name_pattern)

Returns a new NormalEstimate that contains only the parameters whose name matches name_pattern.

Parameters

name_pattern (str) – A regular expression that specifies a pattern for the names.

Type

py:class:~trueq.estimate.NormalEstimate

Readout Estimates

class trueq.estimate.RCalEstimate(key, values, options=None)

Stores estimates for the confusion matrices associated with readout errors.

Parameters
  • key (Key) – A hashable object used for record keeping in analysis. For example, Key(protocol='SRB', n_random_cycles=10).

  • values (dict) – A dict where the keys of the dict are the single qubit labels, and the values are the associated confusion matrices for the probabilities of correctly labeling the qubit’s final state.

  • options (NoneType | dict) – Options that were passed to fit() method.

apply_correction(results, labels=None)

Applies this calibration as a correction to the given results. This is done by inverting each calibration matrix and contracting it onto the corresponding indices of the given results.

import trueq as tq

# make a test and RCAL circuits
rcal_circuits = tq.make_rcal(range(5))
circuit = tq.Circuit([{0: tq.Gate.x}, {range(5): tq.Meas()}])

# construct a simulator with readout error and run the circuits
sim = tq.Simulator().add_readout_error([0.1, 0.05])
sim.run(rcal_circuits, 10000)
sim.run(circuit)

# create an RCAL estimate
rcal_est = rcal_circuits.fit()[0]

# plot original and corrected bitstring distributions
circuit.results.plot()
rcal_est.apply_correction(circuit.results).plot()
../_images/estimate_5_0.png ../_images/estimate_5_1.png

Note

This method necessarily has exponential scaling in the number of qubits. Further, no clipping is performed, so that the output distribution may contain small negative values due to finite sampling effects.

Parameters
  • results (Results) – A results object to correct.

  • labels (Iterable | NoneType) – An optional labelling (iterable of unique non-negative integers) of the bitstrings in the given results object. These labels are used to decide which correction matrices to apply where. If None, the labels from the key of this estimate are assumed.

Returns

A new results object equal to the correction of the input.

Return type

Results

Raises
  • ValueError – If no labels are given and there are no labels in the key of this instance.

  • ValueError – If bitstring lengths are not compatible with the number of labels.

property values

A dict where the keys of the dict are the single qubit labels, and the values are the associated confusion matrices for the probabilities of correctly labeling the qubit’s final state.

Type

dict

Comparison Table

class trueq.estimate.comp_table.CompTable(fit_or_circuits)

Organizes NormalEstimates into a parent-children structure based on twirling groups in a way that makes it easier to compare values.

from itertools import chain, combinations
import trueq as tq

# generate CB circuits
circuits = tq.make_cb({range(3): tq.Gate.x}, [4, 32])
circuits += tq.make_cb({range(2): tq.Gate.x}, [4, 32])
circuits += tq.make_cb({1: tq.Gate.x}, [4, 32])

# use a different twirling group
circuits += tq.make_cb({range(2): tq.Gate.x}, [4, 32], twirl="C")
circuits += tq.make_cb({0: tq.Gate.x}, [4, 32], twirl="C")
circuits += tq.make_cb({1: tq.Gate.x}, [4, 32], twirl="C")

# add a different qubit
circuits += tq.make_cb({range(3): tq.Gate.x}, [4, 32])

# run the circuits on a noisy simulator
tq.Simulator().add_overrotation(0.04).run(circuits)


def powerset(iterable):
    # powerset([1,2,3]) -> () (1,) (2,) (3,) (1, 2) (1, 3) (2, 3) (1, 2, 3)
    s = list(iterable)
    return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))


# get infidelity on every possible combination of labels
fit = circuits.fit(set(chain(powerset(range(3)), powerset((0, 5, 2)))))

# this plotter visualizes CompTable; every subplot is a Family, and every trace
# is a Member
fit.plot.compare_twirl("e_F")
../_images/estimate_6_0.png
Parameters

fit_or_circuits (CircuitCollection | EstimateCollection) – An estimate collection or set of circuits to create the data table for.

class Family(parent, title, labels, names, members, protocols)
labels

Alias for field number 2

members

Alias for field number 4

names

Alias for field number 3

parent

Alias for field number 0

protocols

Alias for field number 5

title

Alias for field number 1

class Member(title, twirls, data, protocol)
data

Alias for field number 2

protocol

Alias for field number 3

title

Alias for field number 0

twirls

Alias for field number 1

table(names=None)

Returns all data in a structure.

import trueq as tq

circuits = tq.make_crosstalk_diagnostics([0, 1, 2], [4, 32])
tq.Simulator().add_overrotation(0.05).run(circuits)
data = tq.estimate.comp_table.CompTable(circuits)

for family in data.table(["p", "r"]):
    print(family.title)
    print(family.labels)
    for member in family.members:
        print(member.title)
        print(member.data)
    print("")
Cliffords on [0, 1, 2]
[(0,), (1,), (2,)]
Simultaneous
{'p': {(0,): EstimateTuple(name='p', val=0.9958988140069571, std=0.000975353771141075), (1,): EstimateTuple(name='p', val=0.9939541511924266, std=0.0018591776790749441), (2,): EstimateTuple(name='p', val=0.993730470507571, std=0.001154380795139388)}}
Isolated
{'p': {(0,): EstimateTuple(name='p', val=0.9925411764051099, std=0.0015591369345274335), (1,): EstimateTuple(name='p', val=0.9951865332958477, std=0.0010311670233690768), (2,): EstimateTuple(name='p', val=0.9949474432003093, std=0.0015889693812041646)}}

Parameters

names (str | Iterable | None) – An estimate parameter of names or list thereof to include in the output. All names are included by default.

Return type

list

KNR Specializations

class trueq.estimate.knr.KnrBodyEstimate(key, names, values, err=None, raw=None, options=None)

Represents a Pauli probability distribution for some subset of qubits of a cycle of interest, as measured by KNR; see also make_knr(). The key stores all relevant metadata, including:

  • key.cycle is the cycle of interest.

  • key.twirl is the twirling group, which may include a superset of the qubits referenced by the cycle.

  • key.labels are the sorted qubit labels defining the subsystem for which this estimate is a distribution over.

  • key.subsystems are the combinations of labels for which marginal distributions are constructed.

property subcycle

A subcycle of this estimate’s cycle of interest containing only the gate(s) that this estimate describes. Any idling qubits (i.e. qubits which are twirled by the protocol but absent from the cycle of interest) that this estimate describes are added as single qubit identity gates.

Type

Cycle

class trueq.estimate.knr.KnrDataTable(fit_or_circuits, group_by=('name', 'cycles', 'twirl'))

Organizes KNR estimates into a table with columns grouped by cycle (and possibly other keywords, see group_by) and rows grouped by error modes.

Parameters
  • fit_or_circuits (CircuitCollection | EstimateCollection) – An estimate collection or set of circuits to create the data table for.

  • group_by (Iterable) – Which keywords to group columns by. For example, if the estimate collection contains data for the same cycle for multiple chips, and this information is distinguished by a “chip” keyword, then this value could be (“cycles”, “chip”, “twirl”). The keywords “cycles” and “twirl” should appear in this list.

class Cell(mean, std, subcycles)

Storage type for the contents of a single cell in the table

mean

Alias for field number 0

std

Alias for field number 1

subcycles

Alias for field number 2

class Row(sort_key, degens, param_names, latex)

Storage type for row descriptions, which index Cell rows.

degens

Alias for field number 1

latex

Alias for field number 3

param_names

Alias for field number 2

sort_key

Alias for field number 0

set_truncation(cutoff, relative_to_max=True, cl=None)

Sets the cutoff at which rows are hidden. If every probability in a subrow falls below the cutoff across an entire row of cells, then these subrows are removed when get_cell() and row_info are called.

Parameters
  • cutoff (float) – The cutoff value to use.

  • relative_to_max (bool) – Whether the cutoff is relative to the maximum probability in the whole table (in which case the cutoff used is cutoff * max_val). Otherwise, the cutoff is absolute.

  • cl (float | NoneType) – The confidence level at which to cutoff, or None to use the current default accessed by get_cl().

property n_cell_rows

The number of cell rows in this table.

Note

This value may change after set_truncation() is called.

Type

int

property n_cell_cols

The number of cell columns in this table.

Type

int

property row_info

A list of Row instances which describe the contents of each cell row of the table.

Note

This value may change after set_truncation() is called.

Type

list

get_cell(idx_cell_row, idx_cell_col)

Gets a single cell by index. This cell contains a matrix of probabilities, a matrix of their standard deviations, and label information for each column in the matrix. Note that row information is found in the corresponding element of row_info, and a header for the whole cell is found in the corresponding element of col_info.

Note

Cell row indices may change when set_truncation() is called.

Parameters
Return type

Cell

property col_info

A list of Col instances which describe the contents of each cell row of the table.

Type

list

row_max(idx_cell_row)

Returns the maximum error probability of the given row and the corresponding standard deviation.

Parameters

idx_cell_row (int) – An index below n_cell_rows.

Return type

tuple

property max

The maximum error probability in the entire table and its corresponding standard deviation.

Type

float

property heights

A list of heights of each cell row.

Type

list

property widths

A list of the maximum widths of each cell column.

Type

list