Estimate¶

`trueq.estimate.base.Estimate`	Base class for all estimate types, which store fit information returned from data analysis.
`trueq.estimate.EstimateCollection`	An iterable container of `Estimate`s with several convenience functions which make it easier to select specific estimates.
`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 `NormalEstimate`s into a parent-children structure based on twirling groups in a way that makes it easier to compare values.
`trueq.estimate.knr_table.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.Estimate(key)¶

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

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).

to_dict()¶

Converts the estimate to a dictionary representation.

Note

The stored Key is also converted to a dictionary.

Return type: dict

classmethod 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

Estimate Collection¶

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.	3.4e-03 (8.7e-04) 0.003413001484111944, 0.0008688607970013571	0.0e+00 (4.7e-09) 0.0, 4.73509005258764e-09
${p}$ Decay parameter of the exponential decay $Ap^m$.	1.0e+00 (1.2e-03) 0.9954493313545174, 0.0011584810626684761	1.0e+00 (5.1e-09) 1.0, 5.05076272276015e-09
${A}$ SPAM parameter of the exponential decay $Ap^m$.	1.0e+00 (7.0e-03) 0.9966855448909828, 0.007046099212143027	1.0e+00 (1.2e-07) 1.0, 1.1517511068997153e-07
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.	3.6e-03 (9.0e-04) 0.0036114424604055673, 0.0008969526209266992	2.7e-05 (1.3e-04) 2.664676431107793e-05, 0.0001288195791247159
${e}_{S}$ The probability of a stochastic error acting on the specified systems during a random gate.	-2.0e-04 (2.2e-04) -0.00019844097629362345, 0.00022272161911552637	-2.7e-05 (1.3e-04) -2.664676431107793e-05, 0.00012881957903769078
${u}$ The unitarity of the noise, that is, the average decrease in the purity of an initial state.	1.0e+00 (5.9e-04) 1.0005292284418779, 0.0005940421765628409	1.0e+00 (2.7e-04) 1.0000568471879172, 0.00027482242488032437
${A}$ SPAM parameter of the exponential decay $Au^m$.	1.0e+00 (6.8e-03) 0.9992518789972346, 0.006815644498438808	1.1e+00 (7.1e-03) 1.061230272533739, 0.007096120955936254

Parameters: estimates (None | 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.

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(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.

Parameters

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 | None

sorted(*names)¶

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

Parameters: names (str) – One or more names 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 (Key | dict) – One or more dict-like objects 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.

values(name, *sort_by, skip_missing=True)¶

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

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()

print(estimate_collection.values("A"))
print(estimate_collection.values("A", "protocol"))

EstimatesTuple(name='A', vals=array([1.00202876, 1.        , 1.00020118, 1.05849829]), stds=array([6.90403184e-03, 1.15175111e-07, 1.04496034e-02, 4.50016567e-03]), sweeps={})
EstimatesTuple(name='A', vals=array([1.00202876, 1.        , 1.00020118, 1.05849829]), stds=array([6.90403184e-03, 1.15175111e-07, 1.04496034e-02, 4.50016567e-03]), sweeps={'protocol': ['SRB', 'SRB', 'XRB', 'XRB']})

Parameters

name (str) – The name of the parameter to extract.
sort_by (str) – The name(s) of the keys to sort values by. The ordering is lexicographical if multiple sort_by names are provided. 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

plot = PlottingSuite(<8 plotters>)¶

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

Normal Estimates¶

class trueq.estimate.NormalEstimate(key, names, values, err=None, decays=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 (None | 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.
decays (None | dict) – Optional, the data used to estimate the values. If not provided, this defaults to an empty dictionary.

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 decays¶

The data used to estimate the values. If decays=None was provided, this will be an empty dictionary.

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

Readout Estimates¶

class trueq.estimate.RCalEstimate(key, values)¶

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.

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()

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 | None) – 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

Comp 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")

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)¶

property labels¶: Alias for field number 2

property members¶: Alias for field number 4

property names¶: Alias for field number 3

property parent¶: Alias for field number 0

property protocols¶: Alias for field number 5

property title¶: Alias for field number 1

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

property data¶: Alias for field number 2

property protocol¶: Alias for field number 3

property title¶: Alias for field number 0

property 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=0.9941353367533439, std=0.001351061121681891), (1,): EstimateTuple(name='p', val=0.9940428213779694, std=0.0012730440147022447), (2,): EstimateTuple(name='p', val=0.9962441951854712, std=0.0009377176553488851)}}
Isolated
{'p': {(0,): EstimateTuple(name='p', val=0.9938061600094177, std=0.0015666284321348646), (1,): EstimateTuple(name='p', val=0.9946301943495421, std=0.001055629432673273), (2,): EstimateTuple(name='p', val=0.9940117333474451, std=0.0015187916132098538)}}

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

KNR Data Table¶

class trueq.estimate.knr_table.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

property mean¶: Alias for field number 0

property std¶: Alias for field number 1

property subcycles¶: Alias for field number 2

class Row(sort_key, degens, param_names, latex)¶

Storage type for row descriptions, which index Cell rows.

property degens¶: Alias for field number 1

property latex¶: Alias for field number 3

property param_names¶: Alias for field number 2

property sort_key¶: Alias for field number 0

set_truncation(cutoff, relative_to_max=True, cl=0.95)¶

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) – The confidence level at which to cutoff.

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

idx_cell_row (int) – An index below n_cell_rows.
idx_cell_col (int) – An index below n_cell_cols.

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