/
simulate.py
1017 lines (799 loc) · 39.4 KB
/
simulate.py
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""" Clifford circuits with Pauli errors simulation functions """
#***************************************************************************************************
# 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.
#***************************************************************************************************
import os as _os
import numpy as _np
import time as _time
from ...tools import symplectic as _symp
from ...objects.label import Label as _Lbl
from ... import objects as _obj
#from . import sample as _samp
_samp = None # MOVED - and this module is deprecated & broken now, so just set to None
def random_paulierror_in_chp(q):
"""
todo.
"""
i = _np.random.randint(0, 3)
if i == 0:
return ('p ' + str(q) + '\n') * 2
elif i == 1:
return 'h ' + str(q) + '\n' + ('p ' + str(q) + '\n') * 2 + 'h ' + str(q) + '\n'
else:
return ('p ' + str(q) + '\n') * 2 + 'h ' + str(q) + '\n' + ('p ' + str(q) + '\n') * 2 + 'h ' + str(q) + '\n'
def random_pauli_in_chp(q):
"""
todo.
"""
i = _np.random.randint(0, 4)
if i == 0:
return ''
elif i == 1:
return ('p ' + str(q) + '\n') * 2
elif i == 2:
return 'h ' + str(q) + '\n' + ('p ' + str(q) + '\n') * 2 + 'h ' + str(q) + '\n'
else:
return ('p ' + str(q) + '\n') * 2 + 'h ' + str(q) + '\n' + ('p ' + str(q) + '\n') * 2 + 'h ' + str(q) + '\n'
def stdgate_to_chp(gate, chpqubits):
"""
todo
Converts any of the standard Clifford gates to a chp string.
"""
gatestr = str(gate).split(':')
name = gatestr[0]
qubits = [chpqubits[q] for q in gatestr[1:]]
if name == 'Gi':
return ''
elif name == 'Gxpi':
s = 'h ' + str(qubits[0]) + '\n'
s += ('p ' + str(qubits[0]) + '\n') * 2
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gypi':
s = ('p ' + str(qubits[0]) + '\n') * 2
s += 'h ' + str(qubits[0]) + '\n'
s += ('p ' + str(qubits[0]) + '\n') * 2
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gzpi':
return ('p ' + str(qubits[0]) + '\n') * 2
elif name == 'Gxpi2':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gxmpi2':
s = 'h ' + str(qubits[0]) + '\n'
s += ('p ' + str(qubits[0]) + '\n') * 3
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gzpi2' or name == 'Gp':
return 'p ' + str(qubits[0]) + '\n'
elif name == 'Gzmpi2' or name == 'Gpdag':
return ('p ' + str(qubits[0]) + '\n') * 3
elif name == 'Gh':
return ('h ' + str(qubits[0]) + '\n')
elif name == 'Gc0':
return ''
elif name == 'Gc1':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc2':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc3':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc4':
s = 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc5':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc6':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc7':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc8':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc9':
s = 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc10':
s = 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc11':
s = 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc12':
s = 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc13':
s = 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc14':
s = 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc15':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc16':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc17':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc18':
s = 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc19':
s = 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc20':
s = 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc21':
s = 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc22':
s = 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'h ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gc23':
s = 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
s += 'p ' + str(qubits[0]) + '\n'
return s
elif name == 'Gcnot':
return 'c ' + str(qubits[0]) + ' ' + str(qubits[1]) + '\n'
elif name == 'Gcphase':
s = 'h ' + str(qubits[1]) + '\n'
s += 'c ' + str(qubits[0]) + ' ' + str(qubits[1]) + '\n'
s = 'h ' + str(qubits[1]) + '\n'
return s
else:
raise ValueError("{} is an unknown gate! You must make your own `gateinchp` function!".format(gate))
class IndDepolErrorModel(object):
"""
todo
"""
def __init__(self, gate_errors, readout_errors):
"""
todo
"""
self.gate_errors = gate_errors
self.readout_errors = readout_errors
def layer_uniform_pauli_probability(self, layer, qubitorder):
"""
todo.
"""
if len(layer) > 0:
return 1 - _np.prod([1 - _np.array([4 * self.gate_errors[gate].get(q, 0) / 3
for q in qubitorder]) for gate in layer], axis=0)
else:
return _np.zeros(len(qubitorder), float)
def readout_uniform_pauli_probability(self, qubitorder):
"""
todo
"""
return 1 - _np.prod([1 - _np.array([4 * self.readout_errors[q1].get(q2, 0) / 3
for q2 in qubitorder]) for q1 in qubitorder], axis=0)
def depolarizing_errors_circuit_simulator(circuitlist, shots, errormodel, gate_to_chp=None,
aux_info_list=None, collision_action='keepseparate',
outdir='', perge_chp_files=True, returnds=True,
verbosity=1):
"""
todo.
"""
if returnds:
ds = _obj.DataSet(collision_action=collision_action)
else:
ds = []
assert(_os.path.isfile("chp")), "This simulator uses the chp.c code.\n" + \
"It must be compiled to an executable called `chp` and situated in this folder!"
try:
_os.mkdir(outdir)
if perge_chp_files:
perge_dir = True
else:
perge_dir = False
except:
perge_dir = False
pass
time0 = _time.time()
if gate_to_chp is None: gate_to_chp = stdgate_to_chp
percentdone = 0
for cind, circuit in enumerate(circuitlist):
print(cind)
time1 = _time.time()
if verbosity > 0:
if verbosity > 1:
print("{0:.2f}".format(cind / len(circuitlist)), end=' ')
else:
if int(_np.floor(cind / len(circuitlist))) > percentdone:
percentdone += 1
print(" - Simulation {} percent complete.".format(percentdone))
n = circuit.num_lines
depth = circuit.depth
# Set up the CHP qubit labels: could be different CHP labels for each circuit.
aschpq = {label: str(i) for i, label in enumerate(circuit.line_labels)}
# A list containing the CHP string for each error-free circuit layer.
perfect_chpstrings = [''.join([gate_to_chp(gate, aschpq) for gate in circuit.layer(i)])
for i in range(depth)]
# Find the probability of error in each circuit layer.
errorprobs = [errormodel.layer_uniform_pauli_probability(circuit.layer(lind), circuit.line_labels)
for lind in range(circuit.depth)]
# Add measurement error at the end
errorprobs.append(errormodel.readout_uniform_pauli_probability(circuit.line_labels))
time2 = _time.time()
for sample in range(shots):
# Sample errors for the circuit. Note that if 1 then a uniformly random Pauli is sampled, so
# there is a 1/4 chance of no error even if this is 1. This is correct.
#print(errorprobs)
haserror = [_np.random.binomial(1, ep) for ep in errorprobs]
#for lind in range(depth):
# print('-', lind)
# print(haserror[lind])
# print([random_pauli_in_chp(q) for q in range(n) if haserror[lind][q] == 1])
# Construct the CHP string for each error layer.
error_chpstrings = [''.join([''] + [random_pauli_in_chp(q) for q in range(n) if haserror[lind][q] == 1])
for lind in range(depth)]
# Interleave the perfect and error CHP strings and then join.
chpstring = '#\n' + ''.join([val for pair in zip(perfect_chpstrings, error_chpstrings) for val in pair])
# Add the readout error
chpstring += ''.join([''] + [random_pauli_in_chp(q) for q in range(n) if haserror[depth][q] == 1])
# Add a measurement on all the qubits.
chpstring += '\n'.join(['m ' + aschpq[q] for q in circuit.line_labels]) + '\n'
#print(chpstring)
with open(outdir + "/circuit-{}-instance-{}.chp".format(cind, sample), 'w') as f:
f.write(chpstring)
# Run CHP on this file.
_os.system("./chp " + outdir + "/circuit-{}-instance-{}.chp > ".format(cind, sample)
+ outdir + "/circuit-{}-instance-{}-out.txt".format(cind, sample))
countdict = {}
for sample in range(shots):
with open(outdir + "/circuit-{}-instance-{}-out.txt".format(cind, sample), 'r') as f:
#print(cind,sample)
outasdict = {}
for i, line in enumerate(f):
if i > 3:
line = line.strip(' \n')
line = line.split(' ')
# todo : this assumes definite outcome circuits, so fix that
# by instead counting forward from the start of the line.
outasdict[circuit.line_labels[int(line[-2][:-1])]] = line[-1]
#print(outasdict)
bitstring = ''.join([outasdict[q] for q in circuit.line_labels])
if perge_chp_files:
_os.system("rm " + outdir + "/circuit-{}-instance-{}.chp".format(cind, sample))
_os.system("rm " + outdir + "/circuit-{}-instance-{}-out.txt".format(cind, sample))
try:
countdict[bitstring] += 1
except:
countdict[bitstring] = 1
if aux_info_list is not None:
aux = aux_info_list[cind]
else:
aux = None
if returnds:
ds.add_count_dict(circuit, countdict, record_zero_counts=False, aux=aux)
else:
ds.append(countdict)
time3 = _time.time()
if verbosity > 1:
print("({0:.2f}, {1:.2f})".format(time2 - time1, time3 - time2), end=', ')
print("Total time: {0:.2f})".format(time3 - time0))
if perge_dir:
_os.system("rmdir " + outdir)
return ds
def circuit_simulator_for_tensored_independent_pauli_errors(circuit, pspec, errormodel, counts,
alloutcomes=False, idle1q_placeholder='I'):
"""
A Clifford circuit simulator for an error model whereby each gate in the circuit induces independent Pauli
errors on some or all of the qubits, with user-specified error probabilities that can vary between gate
and between Pauli. State preparation and measurements errors are restricted to bit-flip errors on the output.
This simulator is a stochastic-unravelling simulator that uses an efficient-in-qubit-number representation
of the action of Clifford gates on Paulis. Specifically, it samples Pauli errors according to the error
statistics provided, and propogates them through the layers of Clifford gates in the circuit using the
conjugation action of the Cliffords on Paulis (as represented by 2n X 2n symplectic matrices for n qubits).
This is repeated for the number of counts (`counts`) requested. So, this function takes a time to run that
scales as (counts * n^2 * circuit depth). Therefore, this method will be slower than the pyGSTi density-matrix
simulators at low qubit number and high `counts`.
Parameters
----------
circuit : Circuit
The circuit to simulate. It should only contain gates that are also contained within the provided
ProcessorSpec `pspec` and are Clifford gates.
pspec : ProcessorSpec
The ProcessorSpec that defines the device. The Clifford model in ProcessorSpec should contain all of
the gates that are in the circuit.
errormodel : dict
A dictionary defining the error model. This errormodel should have keys that are Label objects (the
elements of the circuit). The values for a particular Label is an (n,4) numpy array of floats, that
encodes the errors caused by the gate specified by that Label. The (i,j) value in the array is the
probability that this gate is followed by Pauli i where Pauli 0 = identity, Pauli 1 = X, Pauli 2 = Y
and Pauli 3 = Z. So, if the arrray is [1.,0.,0.,0.] in every row then there is no errors, if it is
[1-p,p/3,p/3,p/3] in row j then there is equal probability of each Pauli error on qubit j with an
error probability of p.
Some simple error models can be auto-constructed using `create_locally_gate_independent_pauli_error_model()`
or create_iid_pauli_error_model()`.
counts : The number of counts, i.e., the number of repeats of the circuit that data should be generated for.
alloutcomes : bool, optional
If True then a dictionary is returned where the keys are all possible outcomes (i.e., all length n
bit strings) and the values are the counts for all of the outcomes. If False, then the returned
dictionary only contains keys for those outcomes that happen at least once.
TODO: docstring: idle1q_placeholder
Returns
-------
dict
A dictionary of simulated measurement outcome counts.
"""
n = circuit.number_of_lines
#TODO REMOVE
#if circuit.identity != idle_name:
# circuit.replace_gatename(circuit.identity,idle_name)
if set(circuit.line_labels) != set(pspec.qubit_labels):
assert(set(circuit.line_labels).issubset(set(pspec.qubit_labels)))
reduced_errormodel = errormodel.copy()
mask = _np.zeros(pspec.number_of_qubits, bool)
for i in range(pspec.number_of_qubits):
if pspec.qubit_labels[i] in circuit.line_labels:
mask[i] = True
for key in list(reduced_errormodel.keys()):
errormatrix = reduced_errormodel[key]
assert(_np.shape(errormatrix)[0] == pspec.number_of_qubits), "Format of `errormodel` incorrect!"
if len(_np.shape(errormatrix)) == 2:
reduced_errormodel[key] = errormatrix[mask, :]
elif len(_np.shape(errormatrix)) == 1:
reduced_errormodel[key] = errormatrix[mask]
else: raise ValueError("Format of `errormodel` incorrect!")
else:
reduced_errormodel = errormodel
results = {}
if alloutcomes:
for i in range(2**n):
result = tuple(_symp.int_to_bitstring(i, n))
results[result] = 0
for i in range(0, counts):
result = oneshot_circuit_simulator_for_tensored_independent_pauli_errors(
circuit, pspec, reduced_errormodel, idle1q_placeholder)
try: results[result] += 1
except: results[result] = 1
return results
def oneshot_circuit_simulator_for_tensored_independent_pauli_errors(circuit, pspec, errormodel, idle1q_placeholder='I'):
"""
Generates a single measurement result for the `circuit_simulator_for_tensored_independent_pauli_errors()`
simulator
Parameters
----------
circuit : Circuit
The circuit to simulate. It should only contain gates that are also contained within the provided
ProcessorSpec `pspec` and are Clifford gates.
pspec : ProcessorSpec
The ProcessorSpec that defines the device. The Clifford model in ProcessorSpec should contain all of
the gates that are in the circuit.
errormodel : dict
A dictionary defining the error model. This errormodel should have keys that are Label objects (the
elements of the circuit). The values for a particular Label is an (n,4) numpy array of floats, that
encodes the errors caused by the gate specified by that Label. The (i,j) value in the array is the
probability that this gate is followed by Pauli i where Pauli 0 = identity, Pauli 1 = X, Pauli 2 = Y
and Pauli 3 = Z. So, if the arrray is [1.,0.,0.,0.] in every row then there is no errors, if it is
[1-p,p/3,p/3,p/3] in row j then there is equal probability of each Pauli error on qubit j with an
error probability of p.
TODO: docstring: idle1q_placeholder
Returns
-------
tuple
A tuple of values that are 0 or 1, corresponding to the results of a z-measurement on all the qubits.
The ordering of this tuple corresponds to the ordering of the wires in the circuit.
"""
n = circuit.number_of_lines
depth = circuit.depth
sout, pout = _symp.prep_stabilizer_state(n, zvals=None)
srep = pspec.models['clifford'].compute_clifford_symplectic_reps()
I = _np.identity(2 * n, int)
for l in range(depth):
layer = circuit.layer_with_idles(l, idle_gate_name=idle1q_placeholder)
s, p = _symp.symplectic_rep_of_clifford_layer(layer, n, q_labels=circuit.line_labels, srep_dict=srep)
# Apply the perfect layer to the current state.
sout, pout = _symp.apply_clifford_to_stabilizer_state(s, p, sout, pout)
# Consider each gate in the layer, and apply Pauli errors with the relevant probs.
for gate in layer:
# Sample a pauli vector for the gate
gerror_p = _np.zeros(2 * n, int)
sampledvec = _np.array([list(_np.random.multinomial(1, pp)) for pp in errormodel[gate]])
# Z and Y both map X - > -X under conjugation, which is encoded with the upper half of
# the p vector being set to 2.
gerror_p[:n] = 2 * (sampledvec[:, 3] ^ sampledvec[:, 2])
# X and Y both map Z - > -Z under conjugation, which is encoded with the lower half of
# the p vector being set to 2.
gerror_p[n:] = 2 * (sampledvec[:, 1] ^ sampledvec[:, 2])
sout, pout = _symp.apply_clifford_to_stabilizer_state(I, gerror_p, sout, pout)
output = []
for q in range(n):
measurement_out = _symp.pauli_z_measurement(sout, pout, q)
# The probability of the '1' outcome
oneprob = measurement_out[1]
# Sample a bit with that probability to be 1.
bit = _np.random.binomial(1, oneprob)
output.append(bit)
# Add measurement errors, by bit-flipping with some probability
try:
measurement_errors = errormodel['measure']
except:
measurement_errors = [0 for i in range(n)]
add_to_outcome = _np.array([_np.random.binomial(1, p) for p in measurement_errors])
output = tuple(_np.array(output) ^ add_to_outcome)
outputasstring = ''
for s in output: outputasstring += str(s)
return outputasstring
def rb_with_pauli_errors(pspec, errormodel, lengths, k, counts, qubit_subset=None, filename=None, rbtype='DRB',
rbspec=[], returndata=True, appenddata=False, verbosity=0, idle1q_placeholder='I'):
"""
Simulates RB with Pauli errors. Can be used to simulated Clifford RB, direct RB and mirror RB. This
function:
1) Samples RB circuits
2) Simulates the RB circuit with the specified Pauli-errors error model
3) Records the summary RB data to file and/or returns this RB data.
Step 1 is implemented using the in-built RB samplers. For more information see rb.sample. Step 2
is implemented using the `circuit_simulator_for_tensored_independent_pauli_errors()` stochastic
errors circuit simulator. See that function for more details.
Parameters
----------
pspec : ProcessorSpec
The ProcessorSpec that defines the device.
errormodel : dict
A dictionary defining the error model. This errormodel should have keys that are Label objects
corresponding to the gates in `pspec`. The values for a particular Label is an (n,4) numpy array of
floats, that encodes the errors caused by the gate specified by that Label. The (i,j) value in the
array is the probability that this gate is followed by Pauli i where Pauli 0 = identity, Pauli 1 = X,
Pauli 2 = Y and Pauli 3 = Z. So, if the arrray is [1.,0.,0.,0.] in every row then there is no errors,
if it is [1-p,p/3,p/3,p/3] in row j then there is equal probability of each Pauli error on qubit j with an
error probability of p.
Some simple error models can be auto-constructed using `create_locally_gate_independent_pauli_error_model()`
or create_iid_pauli_error_model()`.
lengths : list
A list of the RB lengths to sample and simulate circuits at. E.g., for Clifford RB this is the number
of Cliffords in the uncompiled circuit - 2 (see `rb.sample.clifford_rb_circuit()`).
k : int
The number of circuits to sample and simulate at each RB length.
counts : int
The number of counts for each circuit.
qubit_subset : list
If not None, a list of qubit labels that the RB experiment should be over, that is a subset of the
qubits in `pspec`.
filename : str, optional
A filename for where to save the data (if None, the data is not saved to file).
rbtype : {'DRB', 'CRB', 'MRB'}
The RB type to simulate. 'DRB' corresponds to direct RB, 'CRB' corresponds to Clifford RB,
and 'MRB' corresponds to mirror RB.
rbspec : list, optional
Handed to the RB sampling function for all arguments after `pspec` and the RB lengths, which are the first
two arguments handed to the relevant function. See the relevant RB circuit sampling functions for details.
returndata : bool, optional
Whether to return the data
appenddata : bool, optional
If writing to file (i.e., `filename` is not None), whether to append the data to an already existing file
or to write over any existing file.
verbosity : int, optional
The amount of print-to-screen.
Returns
-------
None or RBSummaryDataset
If `returndata` an RBSummaryDataset containing the results. Else, None
"""
assert(rbtype == 'CRB' or rbtype == 'DRB' or rbtype == 'MRB'), "RB type not valid!"
if filename is not None:
if not appenddata:
with open(filename, 'w') as f:
f.write('# Results from a {} simulation\n'.format(rbtype))
f.write('# Number of qubits\n')
if qubit_subset is None: f.write(str(pspec.number_of_qubits))
else: f.write(str(len(qubit_subset)))
f.write('\n# RB length // Success counts // Total counts '
'// Circuit depth // Circuit two-qubit gate count\n')
n = pspec.number_of_qubits
lengthslist = []
scounts = []
cdepths = []
c2Qgcounts = []
for i in range(k):
if verbosity > 0:
print("- Sampling and simulating circuit {} of {} at each of {} lengths".format(i + 1, k, len(lengths)))
print(" - Number of circuits complete = ", end='')
for lind, l in enumerate(lengths):
lengthslist.append(l)
if rbtype == 'DRB':
c, idealout = _samp.direct_rb_circuit(pspec, l, qubit_subset, *rbspec)
elif rbtype == 'CRB':
c, idealout = _samp.clifford_rb_circuit(pspec, l, qubit_subset, *rbspec)
elif rbtype == 'MRB':
c, idealout = _samp.mirror_rb_circuit(pspec, l, qubit_subset, *rbspec)
#if verbosity > 0:
# print(" complete")
# print(" - Simulating circuit...",end='')
outcome = circuit_simulator_for_tensored_independent_pauli_errors(
c, pspec, errormodel, counts, alloutcomes=False, idle1q_placeholder=idle1q_placeholder)
#EGN: Hardcoded 'I' here. Could make this into an arg, but there's really
# no need for the user to modify this unless they use 'I' as a gate label.
if verbosity > 0: print(lind + 1, end=',')
# Add the number of success counts to the list
# scounts.append(outcome.get(idealout,0))
scounts.append(outcome.get(''.join(str(idealbit) for idealbit in idealout), 0))
cdepths.append(c.depth)
c2Qgcounts.append(c.two_q_gate_count())
# Write the data to file in each round.
if filename is not None:
with open(filename, 'a') as f:
f.write('{} {} {} {} {}\n'.format(l, scounts[-1], counts, cdepths[-1], c2Qgcounts[-1]))
if verbosity > 0: print('')
if returndata:
from . import results as _res
data = _res.RBSummaryDataset(n, lengthslist, success_counts=scounts, total_counts=counts,
circuit_depths=cdepths, circuit_twoQgate_counts=c2Qgcounts)
return data
def create_iid_pauli_error_model(pspec, one_qubit_gate_errorrate, two_qubit_gate_errorrate, idle_errorrate,
measurement_errorrate=0., ptype='uniform', idle1q_placeholder='I'):
"""
Returns a dictionary encoding a Pauli-stochastic error model whereby the errors are the same on all the
1-qubit gates, and the same on all 2-qubit gates. The probability of the 3 different Pauli errors on each
qubit is specified by `ptype` and can either be uniform, or always X, Y, or Z errors.
The dictionary returned is in the appropriate format for the
`circuit_simulator_for_tensored_independent_pauli_errors()` circuit simulator function.
Parameters
----------
pspec : ProcessorSpec
The ProcessorSpec that defines the device.
one_qubit_gate_errorrate : float
The 1-qubit gates error rate (the probability of a Pauli error on the target qubit) not including
idle gates.
two_qubit_gate_errorrate : float
The 2-qubit gates error rate (the total probability of a Pauli error on either qubit the gate acts
on -- each qubit has independent errors with equal probabilities).
idle_errorrate : float
The idle gates error rate.
measurement_errorrate : flip
The measurement error rate for all of the qubits. This is the probability that a qubits measurement
result is bit-flipped.
ptype : str, optional
Can be 'uniform', 'X', 'Y' or 'Z'. If 'uniform' then 3 Pauli errors are equally likely, if 'X', 'Y' or
'Z' then the errors are always Pauli X, Y or Z errors, respectively.
TODO: docstring idle1q_placeholder
Returns
-------
dict
An dict that encodes the error model described above in the format required for the simulator
`circuit_simulator_for_tensored_independent_pauli_errors()`.
"""
if ptype == 'uniform':
def error_row(er): return _np.array([1 - er, er / 3, er / 3, er / 3])
elif ptype == 'X':
def error_row(er): return _np.array([1 - er, er, 0., 0.])
elif ptype == 'Y':
def error_row(er): return _np.array([1 - er, 0., er, 0.])
elif ptype == 'Z':
def error_row(er): return _np.array([1 - er, 0., 0., er])
else:
raise ValueError("Error model type not understood! Set `ptype` to a valid option.")
perQ_twoQ_errorrate = 1 - (1 - two_qubit_gate_errorrate)**(1 / 2)
n = pspec.number_of_qubits
errormodel = {}
if idle1q_placeholder is not None:
#Added by EGN: special behavior needed when Model has
# an gate name used to designate a perfect 1-qubit idle op (used as placeholder).
# This translates to a set of "<gatename>:X" operation labels all w/idle_errorrate
idleLbl = idle1q_placeholder
for q in pspec.qubit_labels:
gl = _Lbl(idleLbl, q)
errormodel[gl] = _np.zeros((n, 4), float)
errormodel[gl][:, 0] = _np.ones(n, float)
errormodel[gl][pspec.qubit_labels.index(q), :] = error_row(idle_errorrate)
for gate in pspec.models['clifford'].primitive_op_labels:
errormodel[gate] = _np.zeros((n, 4), float)
errormodel[gate][:, 0] = _np.ones(n, float)
# If not a CNOT, it is a 1-qubit gate / idle.
if gate.number_of_qubits == 2:
q1 = gate.qubits[0]
q2 = gate.qubits[1]
er = perQ_twoQ_errorrate
errormodel[gate][pspec.qubit_labels.index(q1), :] = error_row(er)
errormodel[gate][pspec.qubit_labels.index(q2), :] = error_row(er)
elif gate.number_of_qubits == 1:
q = gate.qubits[0]
# If the idle gate, use the idle error rate
#TODO REMOVE if gate.name == pspec.identity: er = idle_errorrate
er = one_qubit_gate_errorrate
errormodel[gate][pspec.qubit_labels.index(q), :] = error_row(er)
else:
raise ValueError("The ProcessorSpec must only contain 1- and 2- qubit gates!")
errormodel['measure'] = _np.array([measurement_errorrate for q in range(n)])
return errormodel
def create_locally_gate_independent_pauli_error_model(pspec, gate_errorrate_dict, measurement_errorrate_dict={},
ptype='uniform', idle1q_placeholder='I'):
"""
Returns a dictionary encoding a Pauli-stochastic error model whereby the errors are independent of the gates,
with a qubit subject to an error after a circuit layer with the probabilities specified by the dict
`gate_errorrate_dict`. The probability of the 3 different Pauli errors on each qubit is specified by
`ptype` and can either be uniform, or always X, Y, or Z errors.
The dictionary returned is in the appropriate format for the
`circuit_simulator_for_tensored_independent_pauli_errors()` circuit simulator function.
Parameters
----------
pspec : ProcessorSpec
The ProcessorSpec that defines the device.
gate_errorrate_dict : dict
A dict where the keys are elements of pspec.qubit_labels and the values are floats in [0,1].
The element for qubit with label `q` is the error probability for that qubit.
measurement_errorrate_dict : dict
A dict where the keys are elements of pspec.qubit_labels and the values are floats in [0,1].
The element for qubit with label `q` is the measurement bit-flip error probability for that qubit.
All qubits that do not have a measurement error rate specified are assumed to have perfect measurements.
ptype : str, optional
Can be 'uniform', 'X', 'Y' or 'Z'. If 'uniform' then 3 Pauli errors are equally likely, if 'X', 'Y' or
'Z' then the errors are always Pauli X, Y or Z errors, respectively.
TODO: docstring: idle1q_placeholder
Returns
-------
dict
An dict that encodes the error model described above in the format required for the simulator
`circuit_simulator_for_tensored_independent_pauli_errors()`.
"""
if ptype == 'uniform':
def error_row(er): return _np.array([1 - er, er / 3, er / 3, er / 3])
elif ptype == 'X':
def error_row(er): return _np.array([1 - er, er, 0., 0.])
elif ptype == 'Y':
def error_row(er): return _np.array([1 - er, 0., er, 0.])
elif ptype == 'Z':
def error_row(er): return _np.array([1 - er, 0., 0., er])
else:
raise ValueError("Error model type not understood! Set `ptype` to a valid option.")
n = pspec.number_of_qubits
errormodel = {}
if idle1q_placeholder is not None:
#Added by EGN: special behavior needed when Model has
# an gate name used to designate a perfect 1-qubit idle op (used as placeholder).
# This translates to a set of "<gatename>:X" operation labels all w/appropriate errorrate
idleLbl = idle1q_placeholder
for q in pspec.qubit_labels:
gl = _Lbl(idleLbl, q)
er = gate_errorrate_dict[q]
errormodel[gl] = _np.zeros((n, 4), float)
errormodel[gl][:, 0] = _np.ones(n, float)
errormodel[gl][pspec.qubit_labels.index(q), :] = error_row(er)
for gate in pspec.models['clifford'].primitive_op_labels:
errormodel[gate] = _np.zeros((n, 4), float)
errormodel[gate][:, 0] = _np.ones(n, float)
for q in gate.qubits:
er = gate_errorrate_dict[q]
errormodel[gate][pspec.qubit_labels.index(q)] = error_row(er)
errormodel['measure'] = _np.array([measurement_errorrate_dict.get(q, 0.) for q in pspec.qubit_labels])
return errormodel
#
# TODO : DOES THIS NEED AND IDLE PLACEHOLDER?
#
def create_local_pauli_error_model(pspec, one_qubit_gate_errorrate_dict, two_qubit_gate_errorrate_dict,
measurement_errorrate_dict={}, ptype='uniform'):
"""
Returns a dictionary encoding a Pauli-stochastic error model whereby the errors caused by a gate act
only on the "target" qubits of the gate, all the 1-qubit gates on a qubit have the same error rate,
and all the 2-qubit gates on a qubit have the same error rate. The probability of the 3 different Pauli
errors on each qubit is specified by `ptype` and can either be uniform, or always X, Y, or Z errors.
The dictionary returned is in the appropriate format for the
`circuit_simulator_for_tensored_independent_pauli_errors()` circuit simulator function.
Parameters
----------
pspec : ProcessorSpec
The ProcessorSpec that defines the device.
one_qubit_gate_errorrate_dict : dict
A dict where the keys are elements of pspec.qubit_labels and the values are floats in [0,1].
The element for qubit with label `q` is the error probability for all 1-qubit gates on that qubit
two_qubit_gate_errorrate_dict : dict
A dict where the keys are 2-qubit gates in pspec and the values are floats in [0,1]. This is the
error probability for the 2-qubit gate, split evenly into independent Pauli errors on each of the
qubits the gate is intended to act on.
measurement_errorrate_dict : dict
A dict where the keys are elements of pspec.qubit_labels and the values are floats in [0,1].
The element for qubit with label `q` is the measurement bit-flip error probability for that qubit.
All qubits that do not have a measurement error rate specified are assumed to have perfect measurements.
ptype : str, optional
Can be 'uniform', 'X', 'Y' or 'Z'. If 'uniform' then 3 Pauli errors are equally likely, if 'X', 'Y' or
'Z' then the errors are always Pauli X, Y or Z errors, respectively.
Returns
-------
dict
An dict that encodes the error model described above in the format required for the simulator
`circuit_simulator_for_tensored_independent_pauli_errors()`.
"""
if ptype == 'uniform':
def error_row(er): return _np.array([1 - er, er / 3, er / 3, er / 3])
elif ptype == 'X':
def error_row(er): return _np.array([1 - er, er, 0., 0.])
elif ptype == 'Y':
def error_row(er): return _np.array([1 - er, 0., er, 0.])
elif ptype == 'Z':
def error_row(er): return _np.array([1 - er, 0., 0., er])
else:
raise ValueError("Error model type not understood! Set `ptype` to a valid option.")
n = pspec.number_of_qubits