def compute_gate_inverse(gate_label):
if gate_label.name in gate_inverse.keys():
return _lbl.Label(gate_inverse[gate_label.name], gate_label.qubits)
else:
if gate_label.name == 'Gzr' or gate_label.name == 'Gczr':
return _lbl.Label(gate_label.name,
gate_label.qubits,
args=(str(-1 * float(gate_label.args[0])), ))
else:
raise ValueError("Cannot invert gate with name {}".format(
gate_label.name))
def p_expdstr_expop(p):
'''expdstr : expable EXPOP INTEGER'''
plbl = _lbl.Label(p[1]) # just for total sslbls
if len(p[1]) > 0:
p[0] = _lbl.CircuitLabel('', p[1], plbl.sslbls, p[3]),
else:
p[0] = () # special case of {}^power => remain empty
def make_label(s):
if '!' in s:
s, time = s.split(
'!') # must be only two parts (only 1 exclamation pt)
time = float(time)
else:
time = 0.0
if ';' in s:
parts = s.split(';')
parts2 = parts[-1].split(':')
nm = parts[0]
args = parts[1:-1] + [parts2[0]]
sslbls = parts2[1:]
else:
parts = s.split(':')
nm = parts[0]
args = None
sslbls = parts[1:]
if len(sslbls) == 0:
sslbls = None
return _lbl.Label(nm, sslbls, time, args)
def p_layerable_subcircuit_expop(p):
'''layerable : subcircuit EXPOP INTEGER'''
plbl = _lbl.Label(p[1]) # just for total sslbls
p[0] = _lbl.CircuitLabel('', p[1], plbl.sslbls, p[3]),
def p_layerable_subcircuit(p):
'''layerable : subcircuit '''
plbl = _lbl.Label((p[1], )) # just for total sslbls
p[0] = _lbl.CircuitLabel('', p[1], plbl.sslbls, 1),
def simulate_data(model_or_dataset,
circuit_list,
num_samples,
sample_error="multinomial",
seed=None,
rand_state=None,
alias_dict=None,
collision_action="aggregate",
record_zero_counts=True,
comm=None,
mem_limit=None,
times=None):
"""
Creates a DataSet using the probabilities obtained from a model.
Parameters
----------
model_or_dataset : Model or DataSet object
The source of the underlying probabilities used to generate the data.
If a Model, the model whose probabilities generate the data.
If a DataSet, the data set whose frequencies generate the data.
circuit_list : list of (tuples or Circuits) or ExperimentDesign or None
Each tuple or Circuit contains operation labels and
specifies a gate sequence whose counts are included
in the returned DataSet. e.g. ``[ (), ('Gx',), ('Gx','Gy') ]``
If an :class:`ExperimentDesign`, then the design's `.all_circuits_needing_data`
list is used as the circuit list.
num_samples : int or list of ints or None
The simulated number of samples for each circuit. This only has
effect when ``sample_error == "binomial"`` or ``"multinomial"``. If an
integer, all circuits have this number of total samples. If a list,
integer elements specify the number of samples for the corresponding
circuit. If ``None``, then `model_or_dataset` must be a
:class:`~pygsti.objects.DataSet`, and total counts are taken from it
(on a per-circuit basis).
sample_error : string, optional
What type of sample error is included in the counts. Can be:
- "none" - no sample error: counts are floating point numbers such
that the exact probabilty can be found by the ratio of count / total.
- "clip" - no sample error, but clip probabilities to [0,1] so, e.g.,
counts are always positive.
- "round" - same as "clip", except counts are rounded to the nearest
integer.
- "binomial" - the number of counts is taken from a binomial
distribution. Distribution has parameters p = (clipped) probability
of the circuit and n = number of samples. This can only be used
when there are exactly two SPAM labels in model_or_dataset.
- "multinomial" - counts are taken from a multinomial distribution.
Distribution has parameters p_k = (clipped) probability of the gate
string using the k-th SPAM label and n = number of samples.
seed : int, optional
If not ``None``, a seed for numpy's random number generator, which
is used to sample from the binomial or multinomial distribution.
rand_state : numpy.random.RandomState
A RandomState object to generate samples from. Can be useful to set
instead of `seed` if you want reproducible distribution samples across
multiple random function calls but you don't want to bother with
manually incrementing seeds between those calls.
alias_dict : dict, optional
A dictionary mapping single operation labels into tuples of one or more
other operation labels which translate the given circuits before values
are computed using `model_or_dataset`. The resulting Dataset, however,
contains the *un-translated* circuits as keys.
collision_action : {"aggregate", "keepseparate"}
Determines how duplicate circuits are handled by the resulting
`DataSet`. Please see the constructor documentation for `DataSet`.
record_zero_counts : bool, optional
Whether zero-counts are actually recorded (stored) in the returned
DataSet. If False, then zero counts are ignored, except for
potentially registering new outcome labels.
comm : mpi4py.MPI.Comm, optional
When not ``None``, an MPI communicator for distributing the computation
across multiple processors and ensuring that the *same* dataset is
generated on each processor.
mem_limit : int, optional
A rough memory limit in bytes which is used to determine job allocation
when there are multiple processors.
times : iterable, optional
When not None, a list of time-stamps at which data should be sampled.
`num_samples` samples will be simulated at each time value, meaning that
each circuit in `circuit_list` will be evaluated with the given time
value as its *start time*.
Returns
-------
DataSet
A static data set filled with counts for the specified circuits.
"""
NTOL = 10
TOL = 10**-NTOL
if isinstance(model_or_dataset, _ds.DataSet):
dsGen = model_or_dataset
gsGen = None
dataset = _ds.DataSet(
collision_action=collision_action,
outcome_label_indices=dsGen.olIndex) # keep same outcome labels
else:
gsGen = model_or_dataset
dsGen = None
dataset = _ds.DataSet(collision_action=collision_action)
if alias_dict:
alias_dict = {
_lbl.Label(ky): tuple((_lbl.Label(el) for el in val))
for ky, val in alias_dict.items()
} # convert to use Labels
from pygsti.protocols import ExperimentDesign as _ExperimentDesign
if isinstance(circuit_list, _ExperimentDesign):
circuit_list = circuit_list.all_circuits_needing_data
if gsGen and times is None:
if alias_dict is not None:
trans_circuit_list = [
_gstrc.translate_circuit(s, alias_dict) for s in circuit_list
]
else:
trans_circuit_list = circuit_list
all_probs = gsGen.bulk_probabilities(trans_circuit_list,
comm=comm,
mem_limit=mem_limit)
else:
trans_circuit_list = circuit_list
if comm is None or comm.Get_rank() == 0: # only root rank computes
if sample_error in ("binomial", "multinomial") and rand_state is None:
rndm = _rndm.RandomState(seed) # ok if seed is None
else:
rndm = rand_state # can be None
circuit_times = times if times is not None else ["N/A dummy"]
count_lists = _collections.OrderedDict()
for tm in circuit_times:
#print("Time ", tm)
#It would be nice to be able to do something like this (time dependent calc for all probs at ptic time)
#if gsGen and times is not None:
# all_probs = gsGen.bulk_probabilities(trans_circuit_list, comm=comm, mem_limit=mem_limit, time=tm)
for k, (s,
trans_s) in enumerate(zip(circuit_list,
trans_circuit_list)):
if gsGen:
if times is None:
ps = all_probs[trans_s]
else:
ps = gsGen.probabilities(trans_s, time=tm)
if sample_error in ("binomial", "multinomial"):
_adjust_probabilities_inbounds(ps, TOL)
else:
ps = _collections.OrderedDict([
(ol, frac)
for ol, frac in dsGen[trans_s].fractions.items()
])
if gsGen and sample_error in ("binomial", "multinomial"):
_adjust_unit_sum(ps, TOL)
if num_samples is None and dsGen is not None:
N = dsGen[
trans_s].total # use the number of samples from the generating dataset
#Note: total() accounts for other intermediate-measurment branches automatically
else:
try:
N = num_samples[
k] # try to treat num_samples as a list
except:
N = num_samples # if not indexable, num_samples should be a single number
nWeightedSamples = N
counts = _sample_distribution(ps, sample_error,
nWeightedSamples, rndm)
if s not in count_lists: count_lists[s] = []
count_lists[s].append(counts)
if times is None:
for s, counts_list in count_lists.items():
for counts_dict in counts_list:
dataset.add_count_dict(
s, counts_dict, record_zero_counts=record_zero_counts)
else:
for s, counts_list in count_lists.items():
dataset.add_series_data(s,
counts_list,
times,
record_zero_counts=record_zero_counts)
dataset.done_adding_data()
if comm is not None: # broadcast to non-root procs
dataset = comm.bcast(dataset if (comm.Get_rank() == 0) else None,
root=0)
return dataset
def create_mirror_circuit(circ, pspec, circ_type='clifford+zxzxz'):
"""
circ_type : clifford+zxzxz, cz(theta)+zxzxz
"""
n = circ.width
d = circ.depth
pauli_labels = ['I', 'X', 'Y', 'Z']
qubits = circ.line_labels
_, gate_inverse = pspec.compute_one_qubit_gate_relations()
gate_inverse.update(
pspec.compute_multiqubit_inversion_relations()) # add multiQ inverse
assert (circ_type in (
'clifford+zxzxz',
'cz(theta)+zxzxz')), '{} not a valid circ_type!'.format(circ_type)
def compute_gate_inverse(gate_label):
if gate_label.name in gate_inverse.keys():
return _lbl.Label(gate_inverse[gate_label.name], gate_label.qubits)
else:
if gate_label.name == 'Gzr' or gate_label.name == 'Gczr':
return _lbl.Label(gate_label.name,
gate_label.qubits,
args=(str(-1 * float(gate_label.args[0])), ))
else:
raise ValueError("Cannot invert gate with name {}".format(
gate_label.name))
srep_dict = _symp.compute_internal_gate_symplectic_representations(
gllist=['I', 'X', 'Y', 'Z'])
# the `callable` part is a workaround to remove gates with args, defined by functions.
srep_dict.update(
pspec.compute_clifford_symplectic_reps(
tuple((gn for gn, u in pspec.gate_unitaries.items()
if not callable(u)))))
if 'Gxpi2' in pspec.gate_names:
xname = 'Gxpi2'
elif 'Gc16' in pspec.gate_names:
xname = 'Gc16'
else:
raise ValueError(
("There must be an X(pi/2) gate in the processor spec's gate set,"
" and it must be called Gxpi2 or Gc16!"))
assert('Gzr' in pspec.gate_names), \
"There must be an Z(theta) gate in the processor spec's gate set, and it must be called Gzr!"
zrotname = 'Gzr'
if circ_type == 'cz(theta)+zxzxz':
assert('Gczr' in pspec.gate_names), \
"There must be an controlled-Z(theta) gate in the processor spec's gate set, and it must be called Gczr!"
czrotname = 'Gczr'
Xpi2layer = [_lbl.Label(xname, q) for q in qubits]
#make an editable copy of the circuit to add the inverse on to
c = circ.copy(editable=True)
#build the inverse
d_ind = 0
while d_ind < d:
layer = circ.layer(d - d_ind - 1)
if len(layer) > 0 and layer[
0].name == zrotname: # ask if it's a Zrot layer.
# It's necessary for the whole layer to have Zrot gates
#get the entire arbitrary 1q unitaries: Zrot-Xpi/2-Zrot-Xpi/2-Zrot
current_layers = circ[d - d_ind - 5:d - d_ind]
#recompile inverse of current layer
for i in range(n):
if n == 1:
old_params = [(float(current_layers[0].args[0]),
float(current_layers[2].args[0]),
float(current_layers[4].args[0]))
for i in range(n)]
else:
old_params = [(float(current_layers[0][i].args[0]),
float(current_layers[2][i].args[0]),
float(current_layers[4][i].args[0]))
for i in range(n)]
layer_new_params = [
_comp.inv_recompile_unitary(*p) for p in old_params
]
theta1_layer = [
_lbl.Label(zrotname,
qubits[i],
args=(str(layer_new_params[i][0]), ))
for i in range(len(layer_new_params))
]
theta2_layer = [
_lbl.Label(zrotname,
qubits[i],
args=(str(layer_new_params[i][1]), ))
for i in range(len(layer_new_params))
]
theta3_layer = [
_lbl.Label(zrotname,
qubits[i],
args=(str(layer_new_params[i][2]), ))
for i in range(len(layer_new_params))
]
#add to mirror circuit
c.append_circuit_inplace(
_cir.Circuit([theta3_layer], line_labels=circ.line_labels))
c.append_circuit_inplace(
_cir.Circuit([Xpi2layer], line_labels=circ.line_labels))
c.append_circuit_inplace(
_cir.Circuit([theta2_layer], line_labels=circ.line_labels))
c.append_circuit_inplace(
_cir.Circuit([Xpi2layer], line_labels=circ.line_labels))
c.append_circuit_inplace(
_cir.Circuit([theta1_layer], line_labels=circ.line_labels))
d_ind += 5
else:
inverse_layer = [
compute_gate_inverse(gate_label) for gate_label in layer
]
c.append_circuit_inplace(
_cir.Circuit([inverse_layer], line_labels=circ.line_labels))
d_ind += 1
#now that we've built the simple mirror circuit, let's add pauli frame randomization
d_ind = 0
mc = []
net_paulis = {q: 0 for q in qubits}
d = c.depth
correction_angles = {
q: 0
for q in qubits
} # corrections used in the cz(theta) case, which do nothing otherwise.
while d_ind < d:
layer = c.layer(d_ind)
if len(layer) > 0 and layer[
0].name == zrotname: # ask if it's a Zrot layer.
#It's necessary for the whole layer to have Zrot gates
#if the layer is 1Q unitaries, pauli randomize
current_layers = c[d_ind:d_ind + 5]
#generate random pauli
new_paulis = {q: _np.random.randint(0, 4) for q in qubits}
new_paulis_as_layer = [
_lbl.Label(pauli_labels[new_paulis[q]], q) for q in qubits
]
net_paulis_as_layer = [
_lbl.Label(pauli_labels[net_paulis[q]], q) for q in qubits
]
#compute new net pauli based on previous pauli
net_pauli_numbers = _symp.find_pauli_number(
_symp.symplectic_rep_of_clifford_circuit(
_cir.Circuit(new_paulis_as_layer + net_paulis_as_layer,
line_labels=circ.line_labels),
srep_dict=srep_dict)[1])
# THIS WAS THE (THETA) VERSIONS
#net_paulis_as_layer = [_lbl.Label(pauli_labels[net_paulis[q]], q) for q in qubits]
#net_pauli_numbers = _symp.find_pauli_number(_symp.symplectic_rep_of_clifford_circuit(_cir.Circuit(
# new_paulis_as_layer+net_paulis_as_layer), pspec=pspec)[1])
net_paulis = {qubits[i]: net_pauli_numbers[i] for i in range(n)}
#depending on what the net pauli before the U gate is, might need to change parameters on the U gate
# to commute the pauli through
#recompile current layer to account for this and recompile with these paulis
if n == 1:
old_params_and_paulis = [
(float(current_layers[0].args[0]),
float(current_layers[2].args[0]),
float(current_layers[4].args[0]), net_paulis[qubits[i]],
new_paulis[qubits[i]]) for i in range(n)
]
else:
old_params_and_paulis = [
(float(current_layers[0][i].args[0]),
float(current_layers[2][i].args[0]),
float(current_layers[4][i].args[0]),
net_paulis[qubits[i]], new_paulis[qubits[i]])
for i in range(n)
]
layer_new_params = [
_comp.pauli_frame_randomize_unitary(*p)
for p in old_params_and_paulis
]
#recompile any zrotation corrections from the previous Czr into the first zr of this layer. This correction
# will be zero if there are no Czr gates (when it's clifford+zxzxz)
theta1_layer = [
_lbl.Label(zrotname,
qubits[i],
args=(str(layer_new_params[i][0] +
correction_angles[qubits[i]]), ))
for i in range(len(layer_new_params))
]
theta2_layer = [
_lbl.Label(zrotname,
qubits[i],
args=(str(layer_new_params[i][1]), ))
for i in range(len(layer_new_params))
]
theta3_layer = [
_lbl.Label(zrotname,
qubits[i],
args=(str(layer_new_params[i][2]), ))
for i in range(len(layer_new_params))
]
#add to mirror circuit
mc.append([theta1_layer])
mc.append([Xpi2layer])
mc.append([theta2_layer])
mc.append([Xpi2layer])
mc.append([theta3_layer])
d_ind += 5
# reset the correction angles.
correction_angles = {q: 0 for q in qubits}
else:
if circ_type == 'clifford+zxzxz':
net_paulis_as_layer = [
_lbl.Label(pauli_labels[net_paulis[qubits[i]]], qubits[i])
for i in range(n)
]
circ_sandwich = _cir.Circuit(
[layer, net_paulis_as_layer, layer],
line_labels=circ.line_labels)
net_paulis = {
qubits[i]: pn
for i, pn in enumerate(
_symp.find_pauli_number(
_symp.symplectic_rep_of_clifford_circuit(
circ_sandwich, srep_dict=srep_dict)[1]))
}
mc.append(layer)
#we need to account for how the net pauli changes when it gets passed through the clifford layers
if circ_type == 'cz(theta)+zxzxz':
quasi_inv_layer = []
#recompile layer taking into acount paulis
for g in layer:
if g.name == czrotname:
#get the qubits, figure out net pauli on those qubits
gate_qubits = g.qubits
net_paulis_for_gate = (net_paulis[gate_qubits[0]],
net_paulis[gate_qubits[1]])
theta = float(g.args[0])
if ((net_paulis_for_gate[0] % 3 != 0
and net_paulis_for_gate[1] % 3 == 0)
or (net_paulis_for_gate[0] % 3 == 0
and net_paulis_for_gate[1] % 3 != 0)):
theta *= -1
quasi_inv_layer.append(
_lbl.Label(czrotname,
gate_qubits,
args=(str(theta), )))
#for each X or Y, do a Zrotation by -theta on the other qubit after the 2Q gate.
for q in gate_qubits:
if net_paulis[q] == 1 or net_paulis[q] == 2:
for q2 in gate_qubits:
if q2 != q:
correction_angles[q2] += -1 * theta
else:
quasi_inv_layer.append(
_lbl.Label(compute_gate_inverse(g)))
#add to circuit
mc.append([quasi_inv_layer])
#increment position in circuit
d_ind += 1
#update the target pauli
#pauli_layer = [_lbl.Label(pauli_labels[net_paulis[i]], qubits[i]) for i in range(len(qubits))]
# The version from (THETA)
pauli_layer = [_lbl.Label(pauli_labels[net_paulis[q]], q) for q in qubits]
conjugation_circ = _cir.Circuit([pauli_layer],
line_labels=circ.line_labels)
telp_s, telp_p = _symp.symplectic_rep_of_clifford_circuit(
conjugation_circ, srep_dict=srep_dict)
# Calculate the bit string that this mirror circuit should output, from the final telescoped Pauli.
target_bitstring = ''.join(['1' if p == 2 else '0' for p in telp_p[n:]])
mirror_circuit = _cir.Circuit(mc, line_labels=circ.line_labels)
return mirror_circuit, target_bitstring
def _get_next_lbls(s, start, end, create_subcircuits, integerize_sslbls,
segment, interlayer_marker):
if s[start] == "(":
i = start + 1
lbls_list = []
interlayer_marker_inds = []
while i < end and s[i] != ")":
if s[i] == interlayer_marker:
interlayer_marker_inds.append(len(lbls_list) - 1)
i += 1
if i == end or s[i] == ")": break
lbls, i, segment, _ = _get_next_lbls(
s, i, end, create_subcircuits, integerize_sslbls, segment,
interlayer_marker
) # don't recursively look for interlayer markers
lbls_list.extend(lbls)
if i == end: raise ValueError("mismatched parenthesis")
i += 1
exponent, i = _parse_exponent(s, i, end)
if exponent != 1 and len(interlayer_marker_inds) > 0:
if exponent == 0: interlayer_marker_inds = ()
else: # exponent > 1
base_marker_inds = interlayer_marker_inds[:] # a new list
for k in range(1, exponent):
offset = len(lbls_list) * k
interlayer_marker_inds.extend(
map(lambda x: x + offset, base_marker_inds))
if create_subcircuits:
if len(lbls_list) == 0: # special case of {}^power => remain empty
return [], i, segment, ()
else:
tmp = _lbl.Label(
lbls_list
) # just for total sslbs - should probably do something faster
return [
_lbl.CircuitLabel('', lbls_list, tmp.sslbls, exponent)
], i, segment, ()
else:
return lbls_list * exponent, i, segment, interlayer_marker_inds
elif s[start] == "[": # layer
i = start + 1
lbls_list = []
while i < end and s[i] != "]":
lbls, i, segment, _ = _get_next_lbls(
s, i, end, create_subcircuits, integerize_sslbls, segment,
interlayer_marker) # but don't actually look for marker
lbls_list.extend(lbls)
if i == end: raise ValueError("mismatched parenthesis")
i += 1
exponent, i = _parse_exponent(s, i, end)
if len(lbls_list) == 0:
to_exponentiate = _lbl.LabelTupTup(())
elif len(lbls_list) > 1:
time = max([l.time for l in lbls_list])
# create a layer label - a label of the labels within square brackets
to_exponentiate = _lbl.LabelTupTup(tuple(lbls_list)) if (time == 0.0) \
else _lbl.LabelTupTupWithTime(tuple(lbls_list), time)
else:
to_exponentiate = lbls_list[0]
return [to_exponentiate] * exponent, i, segment, ()
else:
lbls, i, segment = _get_next_simple_lbl(s, start, end,
integerize_sslbls, segment)
exponent, i = _parse_exponent(s, i, end)
return lbls * exponent, i, segment, ()