Circuits

`trueq.Circuit`	Represents a quantum `Circuit` to be performed on a device, along with relevant additional information about the circuit.
`trueq.CircuitCollection`	Represents a collection of instances of `Circuit`.

Circuit

class trueq.Circuit(cycles=None, key=None, results=None)

Represents a quantum Circuit to be performed on a device, along with relevant additional information about the circuit.

Note

All True-Q™ circuit generation and analysis methods assume that circuits are run by initializing systems independently in the ground state, applying the parallelized gates (referred to as cycles) obtained by iterating over the circuit, measuring the systems in the computational basis, and populating results with the results.

Specifically, all local gates required to rotate preparations and measurements are already included in cycles.

A circuit is stored as a list of Cycles. It also has private attributes for ease of use with randomized compiling and standard quantum algorithms.

Parameters

cycles (list) – Cycles of the circuit. This is a list of the form, for example, [{(1,): tq.Gate.x, (0, 2): tq.Gate.cz}, ...].
key (Key) – A hashable object used to group similar circuits for analysis. For example, Key(protocol=SRB,n_random_cycles=10).
results (Results | dict) – Results to add to the circuit on construction, see also Circuit.results.

copy(keep_results=True)

Returns a copy of this circuit.

Parameters

keep_results – Whether the copy should include a copy of results. If False, the copied circuit will have no results.
keep_results – bool

Return type

Circuit

measure_all()

Adds a cycle of measurements at the end of the current circuit.

The cycle will be assigned a marker which is 1 larger than the largest marker currently in the circuit.

This adds Meas onto each label currently in the circuit.

Returns: This instance.
Return type: Circuit

property dim

The dimension of each subsystem, e.g. 2 if this circuit acts on qubits. This property is inherited from its cycles or its results if it has no cycles. If this circuit contains no cycles or results, then None.

Type: int | NoneType

property key

Metadata related to this circuit. This is used in analysis methods of protocols, and for organizing circuits within CircuitCollections.

Type: Key

property n_sys

The number of distinct systems participating in this Circuit.

Type: int

property labels

The sorted union of all system labels acted on by gates in this Circuit.

Type: tuple

property n_cycles

The number of Cycles in this Circuit.

Type: int

property cycles

A list of Cycles that represent the Circuit.

Type: list

property results

Results from running this Circuit on a quantum device; a dictionary whose keys are bitstrings, and whose values are total observations of corresponding bitstrings.

Bitstrings are python strings containing $0$ and $1$, e.g., '0110', see Results.

The initial value of this property is {}, which implies that has_results is False.

Note that these values will internally be normalized to their sum, and not to $1$ —for example, with respect to all of our analysis routines, the following results are identical:

{'0': 123, '1': 4}
{'0': 1230, '1': 40}
{'0': 12300, '1': 400}

Type: dict

property has_results

Whether the results property has been set.

Type: bool

append(cycles_or_circuit)

Appends one or more Cycles or another Circuit to this circuit. If another circuit is being appended, its Keys and Resultss cannot conflict with this circuit’s keys or results. Dictionaries are automatically converted to cycles.

Parameters: cycles_or_circuit (list | dict | Cycle | Circuit) – The cycle, list of cycles, or circuit to append.
Returns: This instance.
Return type: Circuit

prepend(cycles_or_circuit)

Prepends one or more Cycles or another Circuit to this circuit. If another circuit is being prepended, its Keys and Resultss cannot conflict with this circuit’s keys or results. Dictionaries are automatically converted to cycles.

Parameters: cycles_or_circuit (list | dict | Cycle | Circuit) – The cycle, list of cycles, or circuit to prepend.
Returns: This instance.
Return type: Circuit

property meas_locs

Returns a list of the locations of all measurements in the Circuit.

The ordering of this list is the same as the expected bit-string order in the circuit’s Resultss.

The order of measurements is decided first by the index of the cycle, e.g.: measurements placed in the first cycle of a circuit are returned before measurements in later cycles. Then the order inside of a cycle is decided by the label on which the measurement is placed, e.g.: measurements placed on label (0,) comes before label (5,)

The returned list contains tuples, where the first entry of tuple is the cycle index, and the second entry of the tuple is the label the measurement is on.

import trueq as tq

# (Cycle 0: measure qubit 0), (Cycle 1: measure qubits 1 and 5)
circ = tq.Circuit([{(0,): tq.Meas()}, {(5,): tq.Meas(), (1,): tq.Meas()}])

# meas_locs returns locations of measurements in the form (cycle, qubit)
circ.meas_locs

[(0, 0), (1, 1), (1, 5)]

Type: list of tuple

property n_meas

The total number of measurement operations in the circuit.

Type: int

draw(interactive=True, filename=None)

Draws the circuit as an SVG. If a filename is provided, writes the SVG to the file.

import trueq as tq

# Make an example SRB circuit
circuit = tq.make_srb([[0, 5], [1, 2], 3, 4], [3], 1)[0]
circuit.draw()

Parameters

interactive (bool) – Determines if the SVG will be interactive or not.
filename (str) – A filename to write the SVG output to.

Return type

trueq.utils.DisplayWrapper

get_probability(outcome, rcal_data=None, labels=None)

Computes the probabilities of the circuit returning specified ditstrings. The ditstrings are adjusted by the 'compiled_pauli' (assumed to be identity by default) and 'measurement_basis' (assumed to be all "Z" by default) of the circuit. These probabilities can be corrected using readout correction data of a given circuit collection. The probabilities of subsystems of the circuits can be calculated by specifying a subset of the labels of the circuit.

import trueq as tq

# generate a circuit collection and gather readout calibration data
rcal_data = tq.make_rcal([0, 1])
sim = tq.Simulator().add_readout_error(0.2)
sim.run(rcal_data, n_shots=10000)

# define and run the circuit of interest
circ = tq.Circuit([{(0,): tq.Gate.x}, {(0, 1): tq.Meas()}])
sim.run(circ, n_shots=1000)

# return the probability of an outcome
circ.get_probability("10", rcal_data)

array([1.03891628])

Parameters

outcome (Iterable) – A list of outcomes, e.g. ["00", "11"], to be estimated. For advanced usage, a list of mixed outcomes and observables, e.g. ["0W01", "XY"], may be specified to estimate expectation values. Each of the observables should be compatible with this circuit’s 'measurement_basis', e.g. by default we can estimate "Z" but not "X" or "Y" on a single qubit.
rcal_data (CircuitCollection) – A circuit collection containing readout calibration circuits used to correct probabilities. By default, this circuit collection is empty. If the circuit collection does not contain compatible readout calibration data, the raw probability will be returned.
labels (Iterable) – The labels of the circuit of which to return the probability. By default, this targets all labels of a circuit.

Returns

The corrected probability of obtaining outcome.

Return type

numpy.ndarray

same_structure(other)

Determines if this circuit has the same structure as the other circuit, that is if it is identical to another circuit except for the parameters of any NativeGates. See also same_structure().

For example:

import trueq as tq
import trueq.compilation as tqc

# create a Compiler
passes = (
    tq.Compiler.NATIVE2Q_PASSES
    + tq.Compiler.RC_PASSES
    + tq.Compiler.HARDWARE_PASSES
)
compiler = tq.Compiler.basic(entangler=tq.Gate.cz, passes=passes)

# define and randomly compile a circuit of interest
circuit = tq.Circuit([{0: tq.Gate.h}, {(0, 1): tq.Gate.cx}]).measure_all()
rc_circuit1 = compiler.compile(circuit)
rc_circuit2 = compiler.compile(circuit)

# the two instances of the randomly compiled circuit have the same structure
# but are not necessarily equal
assert rc_circuit1.same_structure(rc_circuit2)

Parameters: other (Circuit) – Another circuit to compare to.
Return type: bool

to_dict(include_cycles=True)

Returns a dictionary representation of a Circuit object. This dictionary representation contains only base Python classes.

Parameters: include_cycles (bool) – Whether to include the cycles in the dictionary representation.
Return type: dict

static from_dict(dic)

Returns a Circuit constructed from a dictionary representation.

Parameters: dic (dict) – The dictionary representation of the circuit.
Return type: Circuit

to_cirq(metadata=None, passes=None, device=None, join_meas=True)

Converts a Circuit into a cirq.Circuit.

import trueq as tq

# A round trip, to cirq and back
circ = tq.Circuit([{(0, 1): tq.Gate.cx}])
cirq_circ = tq.interface.cirq.from_trueq_circ(circ)
cirq_circ.to_trueq().draw()

Note

By default, arbitrary single-qubit operations are decomposed using the ZXZ QubitMode by default. This may be changed by setting the config to have another mode.

If a gate is provided which is not directly equivalent to a single gate in get_config(), it is decomposed using the Compiler.

Circuit Collection

class trueq.CircuitCollection(circuits=None)

Represents a collection of instances of Circuit.

Parameters: circuits (list | Circuit) – A list of Circuits, or a single Circuit to initially populate this collection with, or None.

property dim

The dimension of each subsystem, e.g. 2 if the circuits in this collection act on qubits. This property is inherited from its circuits. If this collection contains no circuits, then None.

Type: int | NoneType

property n_circuits

The number of Circuits in this collection.

Type: int

property n_sys

The number of systems of all Circuits in this collection.

Type: int

property labels

Which systems are targeted by this Circuit.

Type: tuple

append(circuit)

Appends one or more Circuits to the collection.

Parameters: circuit (Circuit | Iterable) – An instance of Circuit, or any iterable of Circuit objects (including another CircuitCollection).
Returns: This circuit collection.
Return type: CircuitCollection
Raises: ValueError – If the dimension of the circuits to be appended is not compatible with the dimension of the collection.

shuffle(): Randomly shuffles the order of the Circuits contained in this circuit collection in-place.

property n_results

The number of experimental Circuits that have been populated with results, an integer between $0$ and n_circuits.

Type: int

property has_no_results

Whether none of the experimental Circuit's have been populated with data.

Type: bool

property has_some_results

Whether at least one of the experimental Circuits have been populated with data.

Type: bool

property has_all_results

Whether all of the experimental Circuits have been populated with data.

Type: bool

save(filename, overwrite=False, include_cycles=True)

Saves this collection to disk in a binary format, including all results. This collection can be reinstantiated at a later time using trueq.utils.load().

import trueq as tq

circuits = tq.make_srb([0], [4, 20])
tq.Simulator().add_depolarizing(0.01).run(circuits)

# save into a temporary folder for this example
with tq.utils.temp_folder():
    # the extension .tq is arbitrary but recommended
    circuits.save("my_circuits1.tq")

    # save discarding all cycle/gate information for a smaller file size
    circuits.save("my_circuits2.tq", include_cycles=False)

Parameters

filename (str) – A filename to save the circuit collection as.
overwrite (bool) – Any existing file will be overwritten if this is True, else saving will raise FileExistsError
include_cycles (bool) – Whether the cycle information in each circuit should be retained. If false, then all circuits will have their cycles stripped in the saved version to save space. Results and other information will be saved so that analysis and plotting will work on a reinstantiated copy.

fit(labels=None, *, analyze_dim=None, observables=[])

Fits all applicable data found and returns all estimates as an EstimateCollection.

Fits for a subset of data can be obtained by calling fit() on a reduced CircuitCollection by using subset():

import trueq as tq

# make a circuit collection of SRB and XRB circuits
circuits = tq.make_srb([[0], [1, 2]], [4, 32])
circuits += tq.make_xrb([[0], [1, 2]], [4, 32])

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

# fit the results for just SRB
circuits.subset(protocol="SRB").fit()

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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.	2.3e-03 (5.4e-04) 0.002274322579784077, 0.00054094613053108	0.0e+00 (0.0e+00) 0.0, 0.0
${p}$ Decay parameter of the exponential decay $Ap^m$.	1.0e+00 (7.2e-04) 0.9969675698936212, 0.0007212615073747734	1.0e+00 (0.0e+00) 1.0, 0.0
${A}$ SPAM parameter of the exponential decay $Ap^m$.	9.9e-01 (8.1e-03) 0.9933274223266715, 0.008069396553409897	1.0e+00 (0.0e+00) 1.0, 0.0

Many protocols have well defined fits for a strict subset of qubits that the protocol was invoked on. For example, if cycle benchmarking acts on a 10 qubit cycle, fitting 6 of these ten qubits returns a valid estimate of the infidelity on those qubits in the context of the entire cycle. The optional argument labels allows you to customize such subsets of labels.

Certain analysis routines allow optional parameters, see Optional Fitting Arguments.

Parameters

labels (NoneType | Iterable) – A list of lists of qubit labels; fits will be produced on each subset. For convenience, isolated labels are treated as a list of length 1, for example, labels = [[0], [1], [2]] is equivalent to labels = [0, 1, 2]. If no labels are provided (None), they are generated automatically on a protocol-by-protocol basis.
analyze_dim (NoneType) – A small prime to be used as the qudit dimension during analysis. This can be used to account for leakage levels.
observables (list) – A list of strings that specify the observables to compute expectation values of. The supported observables include computational basis states, e.g. '000', and Pauli/Weyl operators, e.g. 'ZIZ' for qubits or 'W01W02W00' for qudits.

Return type

EstimateCollection

property plot

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

Note

If there is a plotting function that you expected to be present but is not, double check the circuit’s Keys to see which types of circuits are present, and also verify that circuits relevant to the plot you want have results entered.

Type: PlottingSuite

batch(max_size, extra_circuits=None, sequencer=None)

Divides the circuits in this collection into a series of batches that are limited in size. This is useful if, for example, your hardware has a finite amount of memory to store circuits with. For this example, we will use True-Q™’s built-in simulator in place of a physical device. In practice, this can be used to batch jobs for submission to hardware, using the modifications suggested in the comments below.

import trueq as tq
import trueq.sequencer as tqs

# make a bunch of circuits
circuits = tq.make_crosstalk_diagnostics([0, 1, 2], [4, 70])

# initialize a simulator to stand in for a physical device
my_device = tq.Simulator()

# Split collection into batches such that there are no more than 80
# circuits in each batch. For a hardware device, the method run() should be
# replaced with a method which runs circuits on the device and returns the
# results for each member of the batch, and the content of this for loop
# would become batch.results = my_device.run(batch) to set the results
# parameter of batch to the returned results.
for batch in circuits.batch(80):
    my_device.run(batch)

# we can also set batch size by total number of cycles or gates
circuits.batch(tqs.NCycles(1000))
circuits.batch(tqs.NGates(1e6))

# We can riffle the circuits by sequence length. Running on a device, we
# again replace the contents of this for loop with
# batch.results = my_device.run(batch) to save the results to the circuit
for batch in circuits.batch(80, sequencer=tqs.RIFFLER):
    my_device.run(batch, overwrite=True)

The batcher also supports automatically adding a fixed set of circuits to each batch. For example, we can add readout calibration circuits to each batch:

import trueq as tq

circuits = tq.make_crosstalk_diagnostics([0, 1, 2], [4, 70])
ro_circuits = tq.make_rcal([0, 1, 2])

# Initialize a simulator to stand in for a physical device. In practice, all
# instances of my_device.run(batch) should be replaced with
# batch.results = my_device.run_on_device(batch) for a device called
# my_device with a function run_on_device() which runs circuits on hardware
# and returns the results.
my_device = tq.Simulator()

for batch in circuits.batch(80, extra_circuits=ro_circuits):
    my_device.run(batch)

Parameters

max_size (int | list | CircuitRuler) – The maximum number of circuits per batch, or any CircuitRuler such as NCycles, py:class:~trueq.sequencer.Gates, or py:class:~trueq.sequencer.TotalTime. You can also specify a list of CircuitRulers; every batch will not surpass any of the rulers’ limits.
extra_circuits (CircuitCollection) – A list of circuits to be placed at the start of every batch. Copies are made of these circuits in every batch.
sequencer (CircuitSequencer) – Specifies how the circuits should be ordered when batching. This is useful, for example, if you want to riffle long circuits with short sequences within a batch, so that all of your long circuits don’t end up in the same batch, see trueq.sequencer.RIFFLER. If no sequencer is given, the existing circuit collection order is used.

Returns

A generator of CircuitCollections.

Return type

generator

copy(keep_results=True)

Returns a copy of this circuit collection, where each circuit is copied using Circuit.copy(). Mutating the new or old circuit collection does not affect the other circuit collection or its circuits.

Parameters

keep_results – Whether each circuit’s copy should include a copy of its results. If False, the copied circuits will have no results.
keep_results – bool

Return type

CircuitCollection

keys(**filter)

Returns a KeySet of unique keys in this CircuitCollection that match the given name/values present in the filter.

Parameters: **filter – Key/value pairs to be used as a filter.
Return type: KeySet

similar_keys(*names, invert=False, **filter)

Returns a generator over disjoint KeySets, where all of the keys in each set have equal values for all names appearing in the given names, and have any specific values specified in filter.

import trueq as tq

circuits = tq.make_srb([0], [4, 10, 50], 30)

print("Grouping keys based on matching 'protocol': ")
for keys in circuits.similar_keys("protocol"):
    print(keys)

print("\nGrouping keys based on 'protocol' and 'n_random_cycles': ")
for keys in circuits.similar_keys("protocol", "n_random_cycles"):
    print(keys)

Grouping keys based on matching 'protocol': 
KeySet(
	Key(compiled_pauli=Weyls('I'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Z'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('X'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Z'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('X'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('X'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Y'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('I'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Z'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('I'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Y'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Y'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)))

Grouping keys based on 'protocol' and 'n_random_cycles': 
KeySet(
	Key(compiled_pauli=Weyls('I'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('X'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Z'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Y'), measurement_basis=Weyls('Z'), n_random_cycles=4, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)))
KeySet(
	Key(compiled_pauli=Weyls('Y'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Z'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('I'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('X'), measurement_basis=Weyls('Z'), n_random_cycles=10, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)))
KeySet(
	Key(compiled_pauli=Weyls('I'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('X'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Y'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)),
	Key(compiled_pauli=Weyls('Z'), measurement_basis=Weyls('Z'), n_random_cycles=50, protocol='SRB', twirl=Twirl({(0,): 'C'}, dim=2)))

Parameters

names (list) – An iterable of key names.
invert (bool) – Whether the list of names to use should be those presented, or all names execpt those presented.
**filter – Key/value pairs to be used as a filter.

Return type

generator

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

Updates every circuit’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 copy() which this method uses.

import trueq as tq

circuits = tq.make_srb(0, [4, 10])

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

# change 'n_random_cycles' to 5 for those circuits where it is 4
circuits.subset(n_random_cycles=4).update_keys(n_random_cycles=5)

circuits.keys()

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KeySet List of all the keys in the KeySet	protocol The characterization protocol used to generate a circuit.	twirl The twirling group used to generate a circuit.	n_random_cycles The number of independent random cycles in the circuit.	compiled_pauli The n-qubit Pauli operator that was compiled into the circuit immediately before measurement.	measurement_basis An n-qubit Pauli operator describing the change-of-basis gates added prior to measurement.	banana
Key Key: banana: 10 compiled_pauli: X measurement_basis: Z n_random_cycles: 5 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	5	X	Z	10
Key Key: banana: 10 compiled_pauli: I measurement_basis: Z n_random_cycles: 5 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	5	I	Z	10
Key Key: banana: 10 compiled_pauli: Z measurement_basis: Z n_random_cycles: 5 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	5	Z	Z	10
Key Key: banana: 10 compiled_pauli: Y measurement_basis: Z n_random_cycles: 5 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	5	Y	Z	10
Key Key: banana: 10 compiled_pauli: Y measurement_basis: Z n_random_cycles: 10 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	10	Y	Z	10
Key Key: banana: 10 compiled_pauli: I measurement_basis: Z n_random_cycles: 10 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	10	I	Z	10
Key Key: banana: 10 compiled_pauli: Z measurement_basis: Z n_random_cycles: 10 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	10	Z	Z	10
Key Key: banana: 10 compiled_pauli: X measurement_basis: Z n_random_cycles: 10 protocol: SRB twirl: Cliffords on [0]	SRB	Cliffords on [0]	10	X	Z	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 circuit collection.

Return type

CircuitCollection

Raises

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

subset(has_results=None, new_collection=True, **filter)

Selects a subset of this collection’s Circuits by matching against a filter. This returns a new CircuitCollection, with references to the original circuits, i.e. no circuit copies are made. Optionally, by using the new_collection flag, the output type can be a generator over circuits that match the filter rather than a new collection object.

import trueq as tq

circuits = tq.make_srb([0], [4, 32])
circuits.append(tq.make_rcal([0]))

# make a new CircuitCollection with only the SRB circuits from the
# CircuitCollection defined above
srb_circuits = circuits.subset(protocol="SRB")
assert srb_circuits.keys().protocol == {"SRB"}

# loop over circuits with no results
# for efficiency, use new_collection=False to avoid new instantiation
for circuit in circuits.subset(has_results=False, new_collection=False):
    circuit.results = {"0": 1}

Parameters

has_results (NoneType | bool) – Whether to filter on circuits which have or do not have results entered. If True, only circuits with results are included, if False, only circuits without results are included, and if None (default) all circuits are included.
new_collection (bool) – Whether to create a new CircuitCollection and return it. Otherwise, a generator over circuits will be returned, which saves memory.
**filter – Key/value pairs to be used as a filter.

Return type

generator | CircuitCollection

to_dict_list(include_cycles=True)

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

Parameters: include_cycles (bool) – Whether to include the cycles in the dictionary representations.
Return type: list

static from_dict_list(lst)

Returns a CircuitCollection constructed from a list of dictionary representations of trueq.Circuits.

Return type: CircuitCollection

batch_size(ruler)

The size of this collection according to the given method of counting circuit size. For example, NCycles, NGates, or TotalTime.

Parameters: ruler (CircuitRuler) – A callable that returns the size of a given circuit.
Return type: int

property results

A list of the Results for every circuit in this collection, where the order of this list matches the order of this collection. This property is also settable, see Circuit.results for allowed formats for setting.

import trueq as tq

circuits = tq.make_srb([0], [4, 100])

print("Circuits start with empty results initially:")
print(circuits.results[:5])

# set all results to the same value
circuits.results = [{"0": 10, "1": 100}] * len(circuits)

print("\nAfter setting the results:")
circuits.results[:5]

Circuits start with empty results initially:
[Results({}, dim=None), Results({}, dim=None), Results({}, dim=None), Results({}, dim=None), Results({}, dim=None)]

After setting the results:

[Results({'0': 10, '1': 100}),
 Results({'0': 10, '1': 100}),
 Results({'0': 10, '1': 100}),
 Results({'0': 10, '1': 100}),
 Results({'0': 10, '1': 100})]

Type: list

sum_results(decompile_paulis=True, **filter)

Sums together all results in the collection that match the given filter.

import trueq as tq

circuits = tq.make_srb([0], [4, 100])
circuits.append(tq.make_xrb([0], [4, 100]))
tq.Simulator().add_overrotation(0.05).run(circuits, n_shots=10)

print("Sum of all results in the circuit collection:")
print(circuits.sum_results())

print("\nTotal number of shots ran for the XRB protocol:")
print(circuits.sum_results(protocol="XRB").n_shots)

print("\nSum of results where `n_random_cycles=4`:")
print(circuits.sum_results(n_random_cycles=4).n_shots)

Sum of all results in the circuit collection:
Results({'0': 1381, '1': 1019})

Total number of shots ran for the XRB protocol:
1800

Sum of results where `n_random_cycles=4`:
1200

Parameters

decompile_paulis (bool) – Whether to adjust ditstrings of each circuit’s results by the 'compiled_pauli' key value of the corresponding circuit when summing. In most cases, and especially for randomized compiling, this value should be True.
**filter – Key/value pairs to be used as a filter.

Returns

The sum of all results.

Return type

Results