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rb.py
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"""
RB Protocol objects
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
from collections import defaultdict
import numpy as _np
from pygsti.protocols import protocol as _proto
from pygsti.protocols import vb as _vb
from pygsti import tools as _tools
from pygsti.algorithms import randomcircuit as _rc
from pygsti.algorithms import rbfit as _rbfit
from pygsti.algorithms import mirroring as _mirroring
class CliffordRBDesign(_vb.BenchmarkingDesign):
"""
Experiment design for Clifford randomized benchmarking.
This encapsulates a "Clifford randomized benchmarking" (CRB) experiment. CRB is the RB protocol defined
in "Scalable and robust randomized benchmarking of quantum processes", Magesan et al. PRL 106 180504 (2011).
The circuits created by this function will respect the connectivity and gate-set of the device encoded by
`pspec` (see the :class:`QubitProcessorSpec` object docstring for how to construct the relevant `pspec`
for a device).
Note that this function uses the convention that a depth "l" CRB circuit consists of "l"+2 Clifford gates
before compilation.
Parameters
----------
pspec : QubitProcessorSpec
The QubitProcessorSpec for the device that the CRB experiment is being generated for, which defines the
"native" gate-set and the connectivity of the device. The returned CRB circuits will be over the gates in
`pspec`, and will respect the connectivity encoded by `pspec`.
clifford_compilations : dict
A dictionary with the potential keys `'absolute'` and `'paulieq'` and corresponding class:`CompilationRules` values.
These compilation rules specify how to compile the "native" gates of `pspec` into Clifford gates.
depths : list of ints
The "CRB depths" of the circuit; a list of integers >= 0. The CRB length is the number of Cliffords in the
circuit - 2 *before* each Clifford is compiled into the native gate-set.
circuits_per_depth : int
The number of (possibly) different CRB circuits sampled at each length.
qubit_labels : list, optional
If not None, a list of the qubits that the RB circuits are to be sampled for. This should
be all or a subset of the qubits in the device specified by the QubitProcessorSpec `pspec`.
If None, it is assumed that the RB circuit should be over all the qubits. Note that the
ordering of this list is the order of the "wires" in the returned circuit, but is otherwise
irrelevant. If desired, a circuit that explicitly idles on the other qubits can be obtained
by using methods of the Circuit object.
randomizeout : bool, optional
If False, the ideal output of the circuits (the "success" or "survival" outcome) is always
the all-zeros bit string. This is probably considered to be the "standard" in CRB. If True,
the ideal output a circuit is randomized to a uniformly random bit-string. This setting is
useful for, e.g., detecting leakage/loss/measurement-bias etc.
interleaved_circuit : Circuit, optional (default None)
Circuit to use in the constuction of an interleaved CRB experiment. When specified each
random clifford operation is interleaved with the specified circuit.
citerations : int, optional
Some of the Clifford compilation algorithms in pyGSTi (including the default algorithm) are
randomized, and the lowest-cost circuit is chosen from all the circuit generated in the
iterations of the algorithm. This is the number of iterations used. The time required to
generate a CRB circuit is linear in `citerations * (CRB length + 2)`. Lower-depth / lower 2-qubit
gate count compilations of the Cliffords are important in order to successfully implement
CRB on more qubits.
compilerargs : list, optional
A list of arguments that are handed to compile_clifford() function, which includes all the
optional arguments of compile_clifford() *after* the `iterations` option (set by `citerations`).
In order, this list should be values for:
* algorithm : str. A string that specifies the compilation algorithm. The default in
compile_clifford() will always be whatever we consider to be the 'best' all-round
algorithm.
* aargs : list. A list of optional arguments for the particular compilation algorithm.
* costfunction : 'str' or function. The cost-function from which the "best" compilation
for a Clifford is chosen from all `citerations` compilations. The default costs a
circuit as 10x the num. of 2-qubit gates in the circuit + 1x the depth of the circuit.
* prefixpaulis : bool. Whether to prefix or append the Paulis on each Clifford.
* paulirandomize : bool. Whether to follow each layer in the Clifford circuit with a
random Pauli on each qubit (compiled into native gates). I.e., if this is True the
native gates are Pauli-randomized. When True, this prevents any coherent errors adding
(on average) inside the layers of each compiled Clifford, at the cost of increased
circuit depth. Defaults to False.
For more information on these options, see the compile_clifford() docstring.
descriptor : str, optional
A string describing the experiment generated, which will be stored in the returned
dictionary.
add_default_protocol : bool, optional
Whether to add a default RB protocol to the experiment design, which can be run
later (once data is taken) by using a :class:`DefaultProtocolRunner` object.
seed : int, optional
A seed to initialize the random number generator used for creating random clifford
circuits.
verbosity : int, optional
If > 0 the number of circuits generated so far is shown.
"""
@classmethod
def from_existing_circuits(cls, data_by_depth, qubit_labels=None,
randomizeout=False, citerations=20, compilerargs=(), interleaved_circuit=None,
descriptor='A Clifford RB experiment', add_default_protocol=False):
"""
Create a :class:`CliffordRBDesign` from an existing set of sampled RB circuits.
This function serves as an alternative to the usual method of creating a Clifford
RB experiment design by sampling a number of circuits randomly. This function
takes a list of previously-sampled random circuits and does not sampling internally.
Parameters
----------
data_by_depth : dict
A dictionary whose keys are integer depths and whose values are lists of
`(circuit, ideal_outcome, num_native_gates)` tuples giving each RB circuit, its
ideal (correct) outcome, and (optionally) the number of native gates in the compiled Cliffords.
If only a 2-tuple is passed, i.e. number of native gates is not included,
the :meth:`average_gates_per_clifford()` function will not work.
qubit_labels : list, optional
If not None, a list of the qubits that the RB circuits are to be sampled for. This should
be all or a subset of the qubits in the device specified by the QubitProcessorSpec `pspec`.
If None, it is assumed that the RB circuit should be over all the qubits. Note that the
ordering of this list is the order of the "wires" in the returned circuit, but is otherwise
irrelevant. If desired, a circuit that explicitly idles on the other qubits can be obtained
by using methods of the Circuit object.
randomizeout : bool, optional
If False, the ideal output of the circuits (the "success" or "survival" outcome) is always
the all-zeros bit string. This is probably considered to be the "standard" in CRB. If True,
the ideal output a circuit is randomized to a uniformly random bit-string. This setting is
useful for, e.g., detecting leakage/loss/measurement-bias etc.
citerations : int, optional
Some of the Clifford compilation algorithms in pyGSTi (including the default algorithm) are
randomized, and the lowest-cost circuit is chosen from all the circuit generated in the
iterations of the algorithm. This is the number of iterations used. The time required to
generate a CRB circuit is linear in `citerations` * (CRB length + 2). Lower-depth / lower 2-qubit
gate count compilations of the Cliffords are important in order to successfully implement
CRB on more qubits.
compilerargs : list, optional
A list of arguments that are handed to compile_clifford() function, which includes all the
optional arguments of compile_clifford() *after* the `iterations` option (set by `citerations`).
In order, this list should be values for:
* algorithm : str. A string that specifies the compilation algorithm. The default in
compile_clifford() will always be whatever we consider to be the 'best' all-round
algorithm.
* aargs : list. A list of optional arguments for the particular compilation algorithm.
* costfunction : 'str' or function. The cost-function from which the "best" compilation
for a Clifford is chosen from all `citerations` compilations. The default costs a
circuit as 10x the num. of 2-qubit gates in the circuit + 1x the depth of the circuit.
* prefixpaulis : bool. Whether to prefix or append the Paulis on each Clifford.
* paulirandomize : bool. Whether to follow each layer in the Clifford circuit with a
random Pauli on each qubit (compiled into native gates). I.e., if this is True the
native gates are Pauli-randomized. When True, this prevents any coherent errors adding
(on average) inside the layers of each compiled Clifford, at the cost of increased
circuit depth. Defaults to False.
For more information on these options, see the compile_clifford() docstring.
descriptor : str, optional
A string describing the experiment generated, which will be stored in the returned
dictionary.
add_default_protocol : bool, optional
Whether to add a default RB protocol to the experiment design, which can be run
later (once data is taken) by using a :class:`DefaultProtocolRunner` object.
Returns
-------
CliffordRBDesign
"""
depths = sorted(list(data_by_depth.keys()))
circuit_lists = [[x[0] for x in data_by_depth[d]] for d in depths]
ideal_outs = [[x[1] for x in data_by_depth[d]] for d in depths]
try:
native_gate_counts = [[x[2] for x in data_by_depth[d]] for d in depths]
except IndexError:
native_gate_counts = None
circuits_per_depth = [len(data_by_depth[d]) for d in depths]
self = cls.__new__(cls)
self._init_foundation(depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
randomizeout, citerations, compilerargs, descriptor, add_default_protocol,
interleaved_circuit, native_gate_counts=native_gate_counts)
return self
def __init__(self, pspec, clifford_compilations, depths, circuits_per_depth, qubit_labels=None, randomizeout=False,
interleaved_circuit=None, citerations=20, compilerargs=(), exact_compilation_key=None,
descriptor='A Clifford RB experiment', add_default_protocol=False, seed=None, verbosity=1, num_processes=1):
if qubit_labels is None: qubit_labels = tuple(pspec.qubit_labels)
circuit_lists = []
ideal_outs = []
native_gate_counts = []
if seed is None:
self.seed = _np.random.randint(1, 1e6) # Pick a random seed
else:
self.seed = seed
for lnum, l in enumerate(depths):
lseed = self.seed + lnum * circuits_per_depth
if verbosity > 0:
print('- Sampling {} circuits at CRB length {} ({} of {} depths) with seed {}'.format(
circuits_per_depth, l, lnum + 1, len(depths), lseed))
args_list = [(pspec, clifford_compilations, l)] * circuits_per_depth
kwargs_list = [dict(qubit_labels=qubit_labels, randomizeout=randomizeout, citerations=citerations,
compilerargs=compilerargs, interleaved_circuit=interleaved_circuit,
seed=lseed + i, return_native_gate_counts=True, exact_compilation_key=exact_compilation_key)
for i in range(circuits_per_depth)]
results = _tools.mptools.starmap_with_kwargs(_rc.create_clifford_rb_circuit, circuits_per_depth,
num_processes, args_list, kwargs_list)
circuits_at_depth = []
idealouts_at_depth = []
native_gate_counts_at_depth = []
for c, iout, nng in results:
circuits_at_depth.append(c)
idealouts_at_depth.append((''.join(map(str, iout)),))
native_gate_counts_at_depth.append(nng)
circuit_lists.append(circuits_at_depth)
ideal_outs.append(idealouts_at_depth)
native_gate_counts.append(native_gate_counts_at_depth)
self._init_foundation(depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
randomizeout, citerations, compilerargs, descriptor, add_default_protocol,
interleaved_circuit, native_gate_counts=native_gate_counts)
def _init_foundation(self, depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
randomizeout, citerations, compilerargs, descriptor, add_default_protocol,
interleaved_circuit, native_gate_counts=None, exact_compilation_key=None):
self.native_gate_count_lists = native_gate_counts
if self.native_gate_count_lists is not None:
# If we have native gate information, pair this with circuit data so that we serialize/truncate properly
self.paired_with_circuit_attrs = ["native_gate_count_lists"]
super().__init__(depths, circuit_lists, ideal_outs, qubit_labels, remove_duplicates=False)
self.circuits_per_depth = circuits_per_depth
self.randomizeout = randomizeout
self.citerations = citerations
self.compilerargs = compilerargs
self.descriptor = descriptor
self.interleaved_circuit = interleaved_circuit
self.exact_compilation_key = exact_compilation_key
if add_default_protocol:
if randomizeout:
defaultfit = 'A-fixed'
else:
defaultfit = 'full'
self.add_default_protocol(RB(name='RB', defaultfit=defaultfit))
#set some auxfile information for interleaved_circuit
self.auxfile_types['interleaved_circuit'] = 'circuit-str-json'
def average_native_gates_per_clifford_for_circuit(self, list_idx, circ_idx):
"""The average number of native gates per Clifford for a specific circuit
Parameters
----------
list_idx: int
The index of the circuit list (for a given depth)
circ_idx: int
The index of the circuit within the circuit list
Returns
-------
avg_gate_counts: dict
The average number of native gates, native 2Q gates, and native size
per Clifford as values with respective label keys
"""
if self.native_gate_counts_lists is None:
raise ValueError("Native gate counts not available, cannot compute average gates per Clifford")
num_clifford_gates = self.depths[list_idx] + 1
avg_gate_counts = {}
for key, native_gate_count in self.native_gate_count_lists[list_idx][circ_idx].items():
avg_gate_counts[key.replace('native', 'avg_native_per_clifford')] = native_gate_count / num_clifford_gates
return avg_gate_counts
def average_native_gates_per_clifford_for_circuit_list(self, list_idx):
"""The average number of gates per Clifford for a circuit list
This essentially gives the average number of native gates per Clifford
for a given depth (indexed by list index, not depth).
Parameters
----------
list_idx: int
The index of the circuit list (for a given depth)
circ_idx: int
The index of the circuit within the circuit list
Returns
-------
float
The average number of native gates per Clifford
"""
if self.native_gate_count_lists is None:
raise ValueError("Native gate counts not available, cannot compute average gates per Clifford")
gate_counts = defaultdict(int)
for native_gate_counts in self.native_gate_count_lists[list_idx]:
for k, v in native_gate_counts.items():
gate_counts[k] += v
num_clifford_gates = len(self.native_gate_count_lists[list_idx]) * (self.depths[list_idx] + 1)
avg_gate_counts = {}
for key, total_native_gate_counts in gate_counts.items():
avg_gate_counts[key.replace('native', 'avg_native_per_clifford')] = total_native_gate_counts / num_clifford_gates
return avg_gate_counts
def average_native_gates_per_clifford(self):
"""The average number of native gates per Clifford for all circuits
Returns
-------
float
The average number of native gates per Clifford
"""
if self.native_gate_count_lists is None:
raise ValueError("Number of native gates not available, cannot compute average gates per Clifford")
gate_counts = defaultdict(int)
num_clifford_gates = 0
for list_idx in range(len(self.depths)):
for native_gate_counts in self.native_gate_count_lists[list_idx]:
for k, v in native_gate_counts.items():
gate_counts[k] += v
num_clifford_gates += len(self.native_gate_count_lists[list_idx]) * (self.depths[list_idx] + 1)
avg_gate_counts = {}
for key, total_native_gate_counts in gate_counts.items():
avg_gate_counts[key.replace('native', 'avg_native_per_clifford')] = total_native_gate_counts / num_clifford_gates
return avg_gate_counts
def map_qubit_labels(self, mapper):
"""
Creates a new experiment design whose circuits' qubit labels are updated according to a given mapping.
Parameters
----------
mapper : dict or function
A dictionary whose keys are the existing self.qubit_labels values
and whose value are the new labels, or a function which takes a
single (existing qubit-label) argument and returns a new qubit-label.
Returns
-------
CliffordRBDesign
"""
mapped_circuits_and_idealouts_by_depth = self._mapped_circuits_and_idealouts_by_depth(mapper)
mapped_qubit_labels = self._mapped_qubit_labels(mapper)
if self.interleaved_circuit is not None:
raise NotImplementedError("TODO: figure out whether `interleaved_circuit` needs to be mapped!")
return CliffordRBDesign.from_existing_circuits(mapped_circuits_and_idealouts_by_depth,
mapped_qubit_labels,
self.randomizeout, self.citerations, self.compilerargs,
self.interleaved_circuit, self.descriptor,
add_default_protocol=False)
class DirectRBDesign(_vb.BenchmarkingDesign):
"""
Experiment design for Direct randomized benchmarking.
This encapsulates a "direct randomized benchmarking" (DRB) experiments. DRB was a protocol
introduced in arXiv:1807.07975 (2018).
An n-qubit DRB circuit consists of (1) a circuit the prepares a uniformly random stabilizer state;
(2) a length-l circuit (specified by `length`) consisting of circuit layers sampled according to
some user-specified distribution (specified by `sampler`), (3) a circuit that maps the output of
the preceeding circuit to a computational basis state. See arXiv:1807.07975 (2018) for further
details.
Parameters
----------
pspec : QubitProcessorSpec
The QubitProcessorSpec for the device that the circuit is being sampled for, which defines the
"native" gate-set and the connectivity of the device. The returned DRB circuit will be over
the gates in `pspec`, and will respect the connectivity encoded by `pspec`. Note that `pspec`
is always handed to the sampler, as the first argument of the sampler function (this is only
of importance when not using an in-built sampler for the "core" of the DRB circuit). Unless
`qubit_labels` is not None, the circuit is sampled over all the qubits in `pspec`.
clifford_compilations : dict
A dictionary with the potential keys `'absolute'` and `'paulieq'` and corresponding
:class:`CompilationRules` values. These compilation rules specify how to compile the
"native" gates of `pspec` into Clifford gates.
depths : int
The set of "direct RB depths" for the circuits. The DRB depths must be integers >= 0.
Unless `addlocal` is True, the DRB length is the depth of the "core" random circuit,
sampled according to `sampler`, specified in step (2) above. If `addlocal` is True,
each layer in the "core" circuit sampled according to "sampler` is followed by a layer of
1-qubit gates, with sampling specified by `lsargs` (and the first layer is proceeded by a
layer of 1-qubit gates), and so the circuit of step (2) is length 2*`length` + 1.
circuits_per_depth : int
The number of (possibly) different DRB circuits sampled at each length.
qubit_labels : list, optional
If not None, a list of the qubits to sample the circuit for. This is a subset of
`pspec.qubit_labels`. If None, the circuit is sampled to act on all the qubits
in `pspec`.
sampler : str or function, optional
If a string, this should be one of: {'edgegrab', pairingQs', 'Qelimination', 'co2Qgates', 'local'}.
Except for 'local', this corresponds to sampling layers according to the sampling function
in rb.sampler named `circuit_layer_by_*` (with `*` replaced by 'sampler'). For 'local', this
corresponds to sampling according to rb.sampler.circuit_layer_of_oneQgates [which is not
a valid form of sampling for n-qubit DRB, but is not explicitly forbidden in this function].
If `sampler` is a function, it should be a function that takes as the first argument a
QubitProcessorSpec, and returns a random circuit layer as a list of gate Label objects. Note that
the default 'Qelimination' is not necessarily the most useful in-built sampler, but it is the
only sampler that requires no parameters beyond the QubitProcessorSpec *and* works for arbitrary
connectivity devices. See the docstrings for each of these samplers for more information.
samplerargs : list, optional
A list of arguments that are handed to the sampler function, specified by `sampler`.
The first argument handed to the sampler is `pspec`, the second argument is `qubit_labels`,
and `samplerargs` lists the remaining arguments handed to the sampler. This is not
optional for some choices of `sampler`.
addlocal : bool, optional
Whether to follow each layer in the "core" circuits, sampled according to `sampler` with
a layer of 1-qubit gates.
lsargs : list, optional
Only used if addlocal is True. A list of optional arguments handed to the 1Q gate
layer sampler circuit_layer_by_oneQgate(). Specifies how to sample 1Q-gate layers.
randomizeout : bool, optional
If False, the ideal output of the circuits (the "success" or "survival" outcome) is the all-zeros
bit string. If True, the ideal output of each circuit is randomized to a uniformly random bit-string.
This setting is useful for, e.g., detecting leakage/loss/measurement-bias etc.
cliffordtwirl : bool, optional
Wether to begin the circuits with a sequence that generates a random stabilizer state. For
standard DRB this should be set to True. There are a variety of reasons why it is better
to have this set to True.
conditionaltwirl : bool, optional
DRB only requires that the initial/final sequences of step (1) and (3) create/measure
a uniformly random / particular stabilizer state, rather than implement a particular unitary.
step (1) and (3) can be achieved by implementing a uniformly random Clifford gate and the
unique inversion Clifford, respectively. This is implemented if `conditionaltwirl` is False.
However, steps (1) and (3) can be implemented much more efficiently than this: the sequences
of (1) and (3) only need to map a particular input state to a particular output state,
if `conditionaltwirl` is True this more efficient option is chosen -- this is option corresponds
to "standard" DRB. (the term "conditional" refers to the fact that in this case we essentially
implementing a particular Clifford conditional on a known input).
citerations : int, optional
Some of the stabilizer state / Clifford compilation algorithms in pyGSTi (including the default
algorithms) are randomized, and the lowest-cost circuit is chosen from all the circuits generated
in the iterations of the algorithm. This is the number of iterations used. The time required to
generate a DRB circuit is linear in `citerations`. Lower-depth / lower 2-qubit gate count
compilations of steps (1) and (3) are important in order to successfully implement DRB on as many
qubits as possible.
compilerargs : list, optional
A list of arguments that are handed to the compile_stabilier_state/measurement()functions (or the
compile_clifford() function if `conditionaltwirl `is False). This includes all the optional
arguments of these functions *after* the `iterations` option (set by `citerations`). For most
purposes the default options will be suitable (or at least near-optimal from the compilation methods
in-built into pyGSTi). See the docstrings of these functions for more information.
partitioned : bool, optional
If False, each circuit is returned as a single full circuit. If True, each circuit is returned as
a list of three circuits consisting of: (1) the stabilizer-prep circuit, (2) the core random circuit,
(3) the pre-measurement circuit. In that case the full circuit is obtained by appended (2) to (1)
and then (3) to (1).
descriptor : str, optional
A description of the experiment being generated. Stored in the output dictionary.
add_default_protocol : bool, optional
Whether to add a default RB protocol to the experiment design, which can be run
later (once data is taken) by using a :class:`DefaultProtocolRunner` object.
seed : int, optional
A seed to initialize the random number generator used for creating random clifford
circuits.
verbosity : int, optional
If > 0 the number of circuits generated so far is shown.
"""
@classmethod
def from_existing_circuits(cls, circuits_and_idealouts_by_depth, qubit_labels=None,
sampler='edgegrab', samplerargs=None, addlocal=False,
lsargs=(), randomizeout=False, cliffordtwirl=True, conditionaltwirl=True,
citerations=20, compilerargs=(), partitioned=False,
descriptor='A DRB experiment', add_default_protocol=False):
"""
Create a :class:`DirectRBDesign` from an existing set of sampled RB circuits.
This function serves as an alternative to the usual method of creating a direct
RB experiment design by sampling a number of circuits randomly. This function
takes a list of previously-sampled random circuits and does not sampling internally.
Parameters
----------
circuits_and_idealouts_by_depth : dict
A dictionary whose keys are integer depths and whose values are lists
of `(circuit, ideal_outcome)` 2-tuples giving each RB circuit and its
ideal (correct) outcome.
qubit_labels : list, optional
If not None, a list of the qubits to sample the circuit for. This is a subset of
`pspec.qubit_labels`. If None, the circuit is sampled to act on all the qubits
in `pspec`.
sampler : str or function, optional
If a string, this should be one of: {'edgegrab', pairingQs', 'Qelimination', 'co2Qgates', 'local'}.
Except for 'local', this corresponds to sampling layers according to the sampling function
in rb.sampler named circuit_layer_by_* (with * replaced by 'sampler'). For 'local', this
corresponds to sampling according to rb.sampler.circuit_layer_of_oneQgates [which is not
a valid form of sampling for n-qubit DRB, but is not explicitly forbidden in this function].
If `sampler` is a function, it should be a function that takes as the first argument a
QubitProcessorSpec, and returns a random circuit layer as a list of gate Label objects. Note that
the default 'Qelimination' is not necessarily the most useful in-built sampler, but it is the
only sampler that requires no parameters beyond the QubitProcessorSpec *and* works for arbitrary
connectivity devices. See the docstrings for each of these samplers for more information.
samplerargs : list, optional
A list of arguments that are handed to the sampler function, specified by `sampler`.
Defaults to [0.25, ].
The first argument handed to the sampler is `pspec`, the second argument is `qubit_labels`,
and `samplerargs` lists the remaining arguments handed to the sampler. This is not
optional for some choices of `sampler`.
addlocal : bool, optional
Whether to follow each layer in the "core" circuits, sampled according to `sampler` with
a layer of 1-qubit gates.
lsargs : list, optional
Only used if addlocal is True. A list of optional arguments handed to the 1Q gate
layer sampler circuit_layer_by_oneQgate(). Specifies how to sample 1Q-gate layers.
randomizeout : bool, optional
If False, the ideal output of the circuits (the "success" or "survival" outcome) is the all-zeros
bit string. If True, the ideal output of each circuit is randomized to a uniformly random bit-string.
This setting is useful for, e.g., detecting leakage/loss/measurement-bias etc.
cliffordtwirl : bool, optional
Wether to begin the circuits with a sequence that generates a random stabilizer state. For
standard DRB this should be set to True. There are a variety of reasons why it is better
to have this set to True.
conditionaltwirl : bool, optional
DRB only requires that the initial/final sequences of step (1) and (3) create/measure
a uniformly random / particular stabilizer state, rather than implement a particular unitary.
step (1) and (3) can be achieved by implementing a uniformly random Clifford gate and the
unique inversion Clifford, respectively. This is implemented if `conditionaltwirl` is False.
However, steps (1) and (3) can be implemented much more efficiently than this: the sequences
of (1) and (3) only need to map a particular input state to a particular output state,
if `conditionaltwirl` is True this more efficient option is chosen -- this is option corresponds
to "standard" DRB. (the term "conditional" refers to the fact that in this case we essentially
implementing a particular Clifford conditional on a known input).
citerations : int, optional
Some of the stabilizer state / Clifford compilation algorithms in pyGSTi (including the default
algorithms) are randomized, and the lowest-cost circuit is chosen from all the circuits generated
in the iterations of the algorithm. This is the number of iterations used. The time required to
generate a DRB circuit is linear in `citerations`. Lower-depth / lower 2-qubit gate count
compilations of steps (1) and (3) are important in order to successfully implement DRB on as many
qubits as possible.
compilerargs : list, optional
A list of arguments that are handed to the compile_stabilier_state/measurement()functions (or the
compile_clifford() function if `conditionaltwirl `is False). This includes all the optional
arguments of these functions *after* the `iterations` option (set by `citerations`). For most
purposes the default options will be suitable (or at least near-optimal from the compilation methods
in-built into pyGSTi). See the docstrings of these functions for more information.
partitioned : bool, optional
If False, each circuit is returned as a single full circuit. If True, each circuit is returned as
a list of three circuits consisting of: (1) the stabilizer-prep circuit, (2) the core random circuit,
(3) the pre-measurement circuit. In that case the full circuit is obtained by appended (2) to (1)
and then (3) to (1).
descriptor : str, optional
A description of the experiment being generated. Stored in the output dictionary.
add_default_protocol : bool, optional
Whether to add a default RB protocol to the experiment design, which can be run
later (once data is taken) by using a :class:`DefaultProtocolRunner` object.
Returns
-------
DirectRBDesign
"""
if samplerargs is None:
samplerargs = [0.25, ]
depths = sorted(list(circuits_and_idealouts_by_depth.keys()))
circuit_lists = [[x[0] for x in circuits_and_idealouts_by_depth[d]] for d in depths]
ideal_outs = [[x[1] for x in circuits_and_idealouts_by_depth[d]] for d in depths]
circuits_per_depth = [len(circuits_and_idealouts_by_depth[d]) for d in depths]
self = cls.__new__(cls)
self._init_foundation(depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
sampler, samplerargs, addlocal, lsargs, randomizeout, cliffordtwirl,
conditionaltwirl, citerations, compilerargs, partitioned, descriptor,
add_default_protocol)
return self
def __init__(self, pspec, clifford_compilations, depths, circuits_per_depth, qubit_labels=None,
sampler='edgegrab', samplerargs=None,
addlocal=False, lsargs=(), randomizeout=False, cliffordtwirl=True, conditionaltwirl=True,
citerations=20, compilerargs=(), partitioned=False, descriptor='A DRB experiment',
add_default_protocol=False, seed=None, verbosity=1, num_processes=1):
if samplerargs is None:
samplerargs = [0.25, ]
if qubit_labels is None: qubit_labels = tuple(pspec.qubit_labels)
circuit_lists = []
ideal_outs = []
if seed is None:
self.seed = _np.random.randint(1, 1e6) # Pick a random seed
else:
self.seed = seed
for lnum, l in enumerate(depths):
lseed = self.seed + lnum * circuits_per_depth
if verbosity > 0:
print('- Sampling {} circuits at DRB length {} ({} of {} depths) with seed {}'.format(
circuits_per_depth, l, lnum + 1, len(depths), lseed))
args_list = [(pspec, clifford_compilations, l)] * circuits_per_depth
kwargs_list = [dict(qubit_labels=qubit_labels, sampler=sampler, samplerargs=samplerargs,
addlocal=addlocal, lsargs=lsargs, randomizeout=randomizeout,
cliffordtwirl=cliffordtwirl, conditionaltwirl=conditionaltwirl,
citerations=citerations, compilerargs=compilerargs,
partitioned=partitioned,
seed=lseed + i) for i in range(circuits_per_depth)]
#results = [_rc.create_direct_rb_circuit(*(args_list[0]), **(kwargs_list[0]))] # num_processes == 1 case
results = _tools.mptools.starmap_with_kwargs(_rc.create_direct_rb_circuit, circuits_per_depth,
num_processes, args_list, kwargs_list)
circuits_at_depth = []
idealouts_at_depth = []
for c, iout in results:
circuits_at_depth.append(c)
idealouts_at_depth.append((''.join(map(str, iout)),))
circuit_lists.append(circuits_at_depth)
ideal_outs.append(idealouts_at_depth)
self._init_foundation(depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
sampler, samplerargs, addlocal, lsargs, randomizeout, cliffordtwirl,
conditionaltwirl, citerations, compilerargs, partitioned, descriptor,
add_default_protocol)
def _init_foundation(self, depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
sampler, samplerargs, addlocal, lsargs, randomizeout, cliffordtwirl,
conditionaltwirl, citerations, compilerargs, partitioned, descriptor,
add_default_protocol):
super().__init__(depths, circuit_lists, ideal_outs, qubit_labels, remove_duplicates=False)
self.circuits_per_depth = circuits_per_depth
self.randomizeout = randomizeout
self.citerations = citerations
self.compilerargs = compilerargs
self.descriptor = descriptor
if isinstance(sampler, str):
self.sampler = sampler
else:
self.sampler = 'function'
self.samplerargs = samplerargs
self.addlocal = addlocal
self.lsargs = lsargs
self.cliffordtwirl = cliffordtwirl
self.conditionaltwirl = conditionaltwirl
self.partitioned = partitioned
if add_default_protocol:
if randomizeout:
defaultfit = 'A-fixed'
else:
defaultfit = 'full'
self.add_default_protocol(RB(name='RB', defaultfit=defaultfit))
def map_qubit_labels(self, mapper):
"""
Creates a new experiment design whose circuits' qubit labels are updated according to a given mapping.
Parameters
----------
mapper : dict or function
A dictionary whose keys are the existing self.qubit_labels values
and whose value are the new labels, or a function which takes a
single (existing qubit-label) argument and returns a new qubit-label.
Returns
-------
DirectRBDesign
"""
mapped_circuits_and_idealouts_by_depth = self._mapped_circuits_and_idealouts_by_depth(mapper)
mapped_qubit_labels = self._mapped_qubit_labels(mapper)
return DirectRBDesign.from_existing_circuits(mapped_circuits_and_idealouts_by_depth,
mapped_qubit_labels,
self.sampler, self.samplerargs, self.addlocal,
self.lsargs, self.randomizeout, self.cliffordtwirl,
self.conditionaltwirl, self.citerations, self.compilerargs,
self.partitioned, self.descriptor, add_default_protocol=False)
class MirrorRBDesign(_vb.BenchmarkingDesign):
"""
Experiment design for mirror randomized benchmarking.
Encapsulates a "mirror randomized benchmarking" (MRB) experiment, for the case of Clifford gates and with
the option of Pauli randomization and local Clifford twirling. To implement mirror RB it is necessary
for U^(-1) to in the gate set for every gate U in the gate set.
**THIS METHOD IS IN DEVELOPEMENT. DO NOT EXPECT THAT THIS FUNCTION WILL BEHAVE THE SAME IN FUTURE RELEASES OF PYGSTI!**
Parameters
----------
pspec : QubitProcessorSpec
The QubitProcessorSpec for the device that the experiment is being generated for. The `pspec` is always
handed to the sampler, as the first argument of the sampler function.
clifford_compilations : dict
A dictionary with the potential keys `'absolute'` and `'paulieq'` and corresponding
:class:`CompilationRules` values. These compilation rules specify how to compile the
"native" gates of `pspec` into Clifford gates.
depths : list of ints
The "mirror RB depths" of the circuits, which is closely related to the circuit depth. A MRB
length must be an even integer, and can be zero.
* If `localclifford` and `paulirandomize` are False, the depth of a sampled circuit = the MRB length.
The first length/2 layers are all sampled independently according to the sampler specified by
`sampler`. The remaining half of the circuit is the "inversion" circuit that is determined
by the first half.
* If `paulirandomize` is True and `localclifford` is False, the depth of a circuit is
`2*length+1` with odd-indexed layers sampled according to the sampler specified by `sampler`, and
the the zeroth layer + the even-indexed layers consisting of random 1-qubit Pauli gates.
* If `paulirandomize` and `localclifford` are True, the depth of a circuit is
`2*length+1 + X` where X is a random variable (between 0 and normally <= ~12-16) that accounts for
the depth from the layer of random 1-qubit Cliffords at the start and end of the circuit.
* If `paulirandomize` is False and `localclifford` is True, the depth of a circuit is
length + X where X is a random variable (between 0 and normally <= ~12-16) that accounts for
the depth from the layer of random 1-qubit Cliffords at the start and end of the circuit.
circuits_per_depth : int
The number of (possibly) different MRB circuits sampled at each length.
qubit_labels : list, optional
If not None, a list of the qubits that the RB circuit is to be sampled for. This should
be all or a subset of the qubits in the device specified by the QubitProcessorSpec `pspec`.
If None, it is assumed that the RB circuit should be over all the qubits. Note that the
ordering of this list is the order of the "wires" in the returned circuit, but is otherwise
irrelevant.
sampler : str or function, optional
If a string, this should be one of: {'edgegrab', 'Qelimination', 'co2Qgates', 'local'}.
Except for 'local', this corresponds to sampling layers according to the sampling function
in rb.sampler named `circuit_layer_by*` (with `*` replaced by 'sampler'). For 'local', this
corresponds to sampling according to rb.sampler.circuit_layer_of_oneQgates [which is not
a valid option for n-qubit MRB -- it results in sim. 1-qubit MRB -- but it is not explicitly
forbidden by this function]. If `sampler` is a function, it should be a function that takes
as the first argument a QubitProcessorSpec, and returns a random circuit layer as a list of gate
Label objects. Note that the default 'Qelimination' is not necessarily the most useful
in-built sampler, but it is the only sampler that requires no parameters beyond the QubitProcessorSpec
*and* works for arbitrary connectivity devices. See the docstrings for each of these samplers
for more information.
samplerargs : list, optional
A list of arguments that are handed to the sampler function, specified by `sampler`.
The first argument handed to the sampler is `pspec` and `samplerargs` lists the
remaining arguments handed to the sampler.
localclifford : bool, optional
Whether to start the circuit with uniformly random 1-qubit Cliffords and all of the
qubits (compiled into the native gates of the device).
paulirandomize : bool, optional
Whether to have uniformly random Pauli operators on all of the qubits before and
after all of the layers in the "out" and "back" random circuits. At length 0 there
is a single layer of random Pauli operators (in between two layers of 1-qubit Clifford
gates if `localclifford` is True); at length l there are 2l+1 Pauli layers as there
are
descriptor : str, optional
A string describing the generated experiment. Stored in the returned dictionary.
add_default_protocol : bool, optional
Whether to add a default RB protocol to the experiment design, which can be run
later (once data is taken) by using a :class:`DefaultProtocolRunner` object.
"""
@classmethod
def from_existing_circuits(cls, circuits_and_idealouts_by_depth, qubit_labels=None,
circuit_type='clifford',
sampler='edgegrab', samplerargs=(0.25, ), localclifford=True,
paulirandomize=True, descriptor='A mirror RB experiment',
add_default_protocol=False):
"""
Create a :class:`MirrorRBDesign` from an existing set of sampled RB circuits.
This function serves as an alternative to the usual method of creating a mirror
RB experiment design by sampling a number of circuits randomly. This function
takes a list of previously-sampled random circuits and does not sampling internally.
Parameters
----------
circuits_and_idealouts_by_depth : dict
A dictionary whose keys are integer depths and whose values are lists
of `(circuit, ideal_outcome)` 2-tuples giving each RB circuit and its
ideal (correct) outcome.
See init docstring for details on all other parameters.
Returns
-------
MirrorRBDesign
"""
depths = sorted(list(circuits_and_idealouts_by_depth.keys()))
circuit_lists = [[x[0] for x in circuits_and_idealouts_by_depth[d]] for d in depths]
ideal_outs = [[x[1] for x in circuits_and_idealouts_by_depth[d]] for d in depths]
circuits_per_depth = [len(circuits_and_idealouts_by_depth[d]) for d in depths]
self = cls.__new__(cls)
self._init_foundation(depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
circuit_type,
sampler, samplerargs, localclifford, paulirandomize, descriptor,
add_default_protocol)
return self
def __init__(self, pspec, depths, circuits_per_depth, qubit_labels=None, circuit_type='clifford',
clifford_compilations=None, sampler='edgegrab', samplerargs=(0.25, ),
localclifford=True, paulirandomize=True, descriptor='A mirror RB experiment',
add_default_protocol=False, seed=None, num_processes=1, verbosity=1):
if qubit_labels is None: qubit_labels = tuple(pspec.qubit_labels)
circuit_lists = []
ideal_outs = []
if seed is None:
self.seed = _np.random.randint(1, 1e6) # Pick a random seed
else:
self.seed = seed
for lnum, l in enumerate(depths):
lseed = self.seed + lnum * circuits_per_depth
if verbosity > 0:
print('- Sampling {} circuits at MRB length {} ({} of {} depths) with seed {}'.format(
circuits_per_depth, l, lnum + 1, len(depths), lseed))
# future: port the starmap functionality to the non-clifford case and merge the two methods
# by just callling `create_mirror_rb_circuit` but with a different argument.
if circuit_type == 'clifford':
args_list = [(pspec, clifford_compilations['absolute'], l)] * circuits_per_depth
kwargs_list = [dict(qubit_labels=qubit_labels, sampler=sampler,
samplerargs=samplerargs, localclifford=localclifford,
paulirandomize=paulirandomize,
seed=lseed + i) for i in range(circuits_per_depth)]
results = _tools.mptools.starmap_with_kwargs(_rc.create_mirror_rb_circuit, circuits_per_depth,
num_processes, args_list, kwargs_list)
elif circuit_type in ('cz+zxzxz-clifford', 'clifford+zxzxz-haar', 'clifford+zxzxz-clifford',
'cz(theta)+zxzxz-haar'):
assert(sampler == 'edgegrab'), "Unless circuit_type = 'clifford' the only valid sampler is 'edgegrab'."
two_q_gate_density = samplerargs[0]
if len(samplerargs) >= 2:
two_q_gate_args_lists = samplerargs[1]
else:
# Default sampler arguments.
two_q_gate_args_lists = {'Gczr': [(str(_np.pi / 2),), (str(-_np.pi / 2),)]}
one_q_gate_type = circuit_type.split('-')[-1]
circs = [_rc.sample_random_cz_zxzxz_circuit(pspec, l // 2, qubit_labels=qubit_labels,
two_q_gate_density=two_q_gate_density,
one_q_gate_type=one_q_gate_type,
two_q_gate_args_lists=two_q_gate_args_lists)
for _ in range(circuits_per_depth)]
mirroring_type = circuit_type.split('-')[0]
if mirroring_type == 'cz+zxzxz':
mirroring_type = 'clifford+zxzxz'
results = [(a, [b]) for a, b in [_mirroring.create_mirror_circuit(c, pspec, circ_type=mirroring_type)
for c in circs]]
else:
raise ValueError('Invalid option for `circuit_type`!')
circuits_at_depth = []
idealouts_at_depth = []
for c, iout in results:
circuits_at_depth.append(c)
idealouts_at_depth.append((''.join(map(str, iout)),))
circuit_lists.append(circuits_at_depth)
ideal_outs.append(idealouts_at_depth)
self._init_foundation(depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
circuit_type, sampler, samplerargs, localclifford, paulirandomize, descriptor,
add_default_protocol, seed=seed)
def _init_foundation(self, depths, circuit_lists, ideal_outs, circuits_per_depth, qubit_labels,
circuit_type, sampler, samplerargs, localclifford, paulirandomize, descriptor,
add_default_protocol, seed=None):
super().__init__(depths, circuit_lists, ideal_outs, qubit_labels, remove_duplicates=False)
self.circuits_per_depth = circuits_per_depth
self.descriptor = descriptor
self.circuit_type = circuit_type
self.sampler = sampler
self.samplerargs = samplerargs
self.localclifford = localclifford
self.paulirandomize = paulirandomize
self.seed = seed
if add_default_protocol:
self.add_default_protocol(RB(name='RB', datatype='adjusted_success_probabilities', defaultfit='A-fixed'))
def map_qubit_labels(self, mapper):
"""
Creates a new experiment design whose circuits' qubit labels are updated according to a given mapping.
Parameters
----------
mapper : dict or function
A dictionary whose keys are the existing self.qubit_labels values
and whose value are the new labels, or a function which takes a
single (existing qubit-label) argument and returns a new qubit-label.
Returns
-------
MirrorRBDesign
"""
mapped_circuits_and_idealouts_by_depth = self._mapped_circuits_and_idealouts_by_depth(mapper)
mapped_qubit_labels = self._mapped_qubit_labels(mapper)
return DirectRBDesign.from_existing_circuits(mapped_circuits_and_idealouts_by_depth,
mapped_qubit_labels,
self.circuit_type, self.sampler,
self.samplerargs, self.localclifford,
self.paulirandomize, self.descriptor,
add_default_protocol=False)
class BinaryRBDesign(_vb.BenchmarkingDesign):
"""
Experiment design for binary randomized benchmarking.
Encapsulates a "binary randomized benchmarking" (BiRB) experiment.
Parameters
----------
pspec : QubitProcessorSpec
The QubitProcessorSpec for the device that the experiment is being generated for. The `pspec` is always
handed to the sampler, as the first argument of the sampler function.
clifford_compilation: CompilationRules
Rules for exactly (absolutely) compiling the "native" gates of `pspec` into Clifford gates.
depths : list of ints
The "benchmark depth" of the circuit, which is the number of randomly sampled layers of gates in
the core circuit. The full BiRB circuit has depth=length+2.
circuits_per_depth : int
The number of (possibly) different MRB circuits sampled at each length.
qubit_labels : list, optional
If not None, a list of the qubits that the RB circuit is to be sampled for. This should
be all or a subset of the qubits in the device specified by the QubitProcessorSpec `pspec`.
If None, it is assumed that the RB circuit should be over all the qubits. Note that the
ordering of this list is the order of the ``wires'' in the returned circuit, but is otherwise
irrelevant.
layer_sampling: str, optional