def randomized_benchmarking(qubit_name, nr_cliffords, nr_seeds,
net_clifford=0, restless=False,
label='randomized_benchmarking',
cal_points=True,
double_curves=True):
'''
Input pars:
nr_cliffords: list nr_cliffords for which to generate RB seqs
nr_seeds: int nr_seeds for which to generate RB seqs
net_clifford: int index of net clifford the sequence should perform
0 corresponds to Identity and 3 corresponds to X180
restless: bool, does not initialize if restless is True
label: some string that can be used as a label.
cal_points: bool whether to replace the last two elements with
calibration points, set to False if you want
to measure a single element (for e.g. optimization)
double_curves: Alternates between net clifford 0 and 3
returns:
qasm_file
generates a qasm file for single qubit Clifford based randomized
benchmarking.
'''
net_cliffords = [0, 3] # Exists purely for the double curves mode
filename = join(base_qasm_path, label+'.qasm')
qasm_file = mopen(filename, mode='w')
qasm_file.writelines('qubit {} \n'.format(qubit_name))
i = 0
for seed in range(nr_seeds):
for j, n_cl in enumerate(nr_cliffords):
if not restless:
qasm_file.writelines('init_all \n')
if cal_points and (j == (len(nr_cliffords)-4) or
j == (len(nr_cliffords)-3)):
qasm_file.writelines('RO {} \n'.format(qubit_name))
elif cal_points and (j == (len(nr_cliffords)-2) or
j == (len(nr_cliffords)-1)):
qasm_file.writelines('X180 {} \n'.format(qubit_name))
qasm_file.writelines('RO {} \n'.format(qubit_name))
else:
if double_curves:
net_clifford = net_cliffords[i % 2]
i += 1
cl_seq = rb.randomized_benchmarking_sequence(
n_cl, desired_net_cl=net_clifford)
pulse_keys = rb.decompose_clifford_seq(cl_seq)
for pulse in pulse_keys:
if pulse != 'I':
qasm_file.writelines('{} {}\n'.format(
pulse, qubit_name))
qasm_file.writelines('RO {} \n'.format(qubit_name))
qasm_file.close()
return qasm_file
def rb_block(self, sp1d_idx, sp2d_idx, **kw):
"""
Creates simultaneous blocks of RB pulses for each task in
preprocessed_task_list.
Args:
sp1d_idx (int): current iteration index in the 1D sweep points array
sp2d_idx (int): current iteration index in the 2D sweep points array
Keyword args:
interleaved_gate (see docstring of parent class)
"""
interleaved_gate = kw.get('interleaved_gate', None)
pulse_op_codes_list = []
tl = [self.preprocessed_task_list[0]] if self.identical_pulses else \
self.preprocessed_task_list
for i, task in enumerate(tl):
param_name = 'seeds' if interleaved_gate is None else 'seeds_irb'
seed_idx = sp1d_idx if self.sweep_type['seeds'] == 0 else sp2d_idx
clf_idx = sp1d_idx if self.sweep_type[
'cliffords'] == 0 else sp2d_idx
seed = task['sweep_points'].get_sweep_params_property(
'values', self.sweep_type['seeds'], param_name)[seed_idx,
clf_idx]
clifford = task['sweep_points'].get_sweep_params_property(
'values', self.sweep_type['cliffords'], 'cliffords')[clf_idx]
cl_seq = rb.randomized_benchmarking_sequence(
clifford, seed=seed, interleaved_gate=interleaved_gate)
pulse_list = rb.decompose_clifford_seq(
cl_seq, gate_decomp=self.gate_decomposition)
if self.purity:
idx = sp1d_idx if self.sweep_type['seeds'] == 0 else sp2d_idx
pulse_list += [self.tomo_pulses[idx % 3]]
pulse_op_codes_list += [pulse_list]
rb_block_list = [
self.block_from_ops(f"rb_{task['qb']}", [
f"{p} {task['qb']}"
for p in pulse_op_codes_list[0 if self.identical_pulses else i]
]) for i, task in enumerate(self.preprocessed_task_list)
]
return self.simultaneous_blocks(f'sim_rb_{sp1d_idx}{sp1d_idx}',
rb_block_list,
block_align='end',
destroy=self.fast_mode)
def test_recovery_Y180_irb(self):
cliffords = [0, 1, 50, 100]
nr_seeds = 100
for cl in cliffords:
for _ in range(nr_seeds):
cl_seq = rb.randomized_benchmarking_sequence(
cl, desired_net_cl=0, interleaved_gate='Y180')
for decomp in ['HZ', 'XY']:
pulse_keys = rb.decompose_clifford_seq(cl_seq,
gate_decomp=decomp)
gproduct = qtp.tensor(qtp.identity(2))
for pk in pulse_keys:
gproduct = self.standard_pulses[pk] * gproduct
x = gproduct.full() / gproduct.full()[0][0]
self.assertTrue(
np.all((np.allclose(np.real(x), np.eye(2)),
np.allclose(np.imag(x), np.zeros(2)))))
def Randomized_Benchmarking_seq(pulse_pars, RO_pars,
nr_cliffords,
nr_seeds,
net_clifford=0,
post_msmt_delay=3e-6,
cal_points=True,
resetless=False,
double_curves=False,
seq_name=None,
verbose=False, upload=True):
'''
Input pars:
pulse_pars: dict containing pulse pars
RO_pars: dict containing RO pars
nr_cliffords: list nr_cliffords for which to generate RB seqs
nr_seeds: int nr_seeds for which to generate RB seqs
net_clifford: int index of net clifford the sequence should perform
0 corresponds to Identity and 3 corresponds to X180
post_msmt_delay:
cal_points: bool whether to replace the last two elements with
calibration points, set to False if you want
to measure a single element (for e.g. optimization)
resetless: bool if False will append extra Id element if seq
is longer than 50us to ensure proper initialization
double_curves: Alternates between net clifford 0 and 3
upload: Upload to the AWG
returns:
seq, elements_list
Conventional use:
nr_cliffords = [n1, n2, n3 ....]
cal_points = True
Optimization use (resetless or not):
nr_cliffords = [n] is a list with a single entry
cal_points = False
net_clifford = 3 (optional) make sure it does a net pi-pulse
post_msmt_delay is set (optional)
resetless (optional)
'''
if seq_name is None:
seq_name = 'RandomizedBenchmarking_sequence'
seq = sequence.Sequence(seq_name)
station.pulsar.update_channel_settings()
el_list = []
pulses = get_pulse_dict_from_pars(pulse_pars)
net_cliffords = [0, 3] # Exists purely for the double curves mode
i = 0
for seed in range(nr_seeds):
for j, n_cl in enumerate(nr_cliffords):
if double_curves:
net_clifford = net_cliffords[i%2]
i += 1 # only used for ensuring unique elt names
if cal_points and (j == (len(nr_cliffords)-4) or
j == (len(nr_cliffords)-3)):
el = multi_pulse_elt(i, station,
[pulses['I'], RO_pars])
elif cal_points and (j == (len(nr_cliffords)-2) or
j == (len(nr_cliffords)-1)):
el = multi_pulse_elt(i, station,
[pulses['X180'], RO_pars])
else:
cl_seq = rb.randomized_benchmarking_sequence(
n_cl, desired_net_cl=net_clifford)
pulse_keys = rb.decompose_clifford_seq(cl_seq)
pulse_list = [pulses[x] for x in pulse_keys]
pulse_list += [RO_pars]
# copy first element and set extra wait
pulse_list[0] = deepcopy(pulse_list[0])
pulse_list[0]['pulse_delay'] += post_msmt_delay
el = multi_pulse_elt(i, station, pulse_list)
el_list.append(el)
seq.append_element(el, trigger_wait=True)
# If the element is too long, add in an extra wait elt
# to skip a trigger
if resetless and n_cl*pulse_pars['pulse_delay']*1.875 > 50e-6:
el = multi_pulse_elt(i, station, [pulses['I']])
el_list.append(el)
seq.append_element(el, trigger_wait=True)
if upload:
station.components['AWG'].stop()
station.pulsar.program_awg(seq, *el_list, verbose=verbose)
return seq, el_list
else:
return seq, el_list
def randomized_renchmarking_seqs(qb_name,
operation_dict,
cliffords,
nr_seeds=None,
net_clifford=0,
gate_decomposition='HZ',
interleaved_gate=None,
upload=True,
cl_sequence=None,
sampling_seeds=None,
cal_points=None,
prep_params=dict()):
"""
Args
qb_name (str): name of qubit
operation_dict (dict): dict with all pulse dicts of qubit
cliffords (array): array of ints specifying the number of random
Cliffords to generate in each sequence
nr_seeds (array): array of the form np.arange(nr_seeds_value)
net_clifford (int): 0 or 1; whether the recovery Clifford returns
qubits to ground statea (0) or puts them in the excited states (1)
gate_decomposition (str): the decomposition of Clifford gates
into primitives; can be "XY", "HZ", or "5Primitives"
interleaved_gate (str): pycqed name for a gate
upload (bool): whether to upload sequence to AWGs
cl_sequence (list): the Clifford sequence to use for all seeds. Can
also be lists of lists in which case the user must ensure that
len(nr seeds) % len(cl_sequence) == 0.
sampling_seeds (array of ints): ints that will be used as seeds for
the random generation of Cliffords. Should have the same length
as nr_seeds.
cal_points (CalibrationPoints): instance of CalibrationPoints
prep_params (dict): qubit preparation_params dict
"""
seq_name = '1Qb_RB_sequence'
if sampling_seeds is None:
if nr_seeds is None:
raise ValueError('Please provide either "sampling_seeds" or '
'"nr_seeds."')
sampling_seeds = [None] * len(nr_seeds)
else:
nr_seeds = np.arange(len(sampling_seeds))
if cl_sequence is not None:
if isinstance(cl_sequence[0], list):
# if cl_sequence is a list of lists such that
# len(nr_seeds) != len(cl_sequence) but
# len(nr_seeds) % len(cl_sequence) == 0,
# then create as many copies of the lists in cl_sequence until
# len(cl_sequence) == len(nr_seeds).
assert len(nr_seeds) % len(cl_sequence) == 0
k = len(nr_seeds) // len(cl_sequence)
cl_seq_temp = k * cl_sequence
sequences = []
for nCl in cliffords:
pulse_list_list_all = []
for s in nr_seeds:
if cl_sequence is None:
cl_seq = rb.randomized_benchmarking_sequence(
nCl,
desired_net_cl=net_clifford,
interleaved_gate=interleaved_gate,
seed=sampling_seeds[s])
elif isinstance(cl_sequence[0], list):
cl_seq = cl_seq_temp[s]
else:
cl_seq = cl_sequence
pulse_keys = rb.decompose_clifford_seq(
cl_seq, gate_decomp=gate_decomposition)
# to avoid having only virtual gates in segment:
pulse_keys = ['I'] + pulse_keys
pulse_list = [
operation_dict[x + ' ' + qb_name] for x in pulse_keys
]
pulse_list += [operation_dict['RO ' + qb_name]]
pulse_list_w_prep = add_preparation_pulses(pulse_list,
operation_dict,
[qb_name],
**prep_params)
pulse_list_list_all.append(pulse_list_w_prep)
seq = pulse_list_list_seq(pulse_list_list_all,
seq_name + f'_{nCl}',
upload=False)
if cal_points is not None:
seq.extend(
cal_points.create_segments(operation_dict, **prep_params))
sequences.append(seq)
# reuse sequencer memory by repeating readout pattern
[s.repeat_ro(f"RO {qb_name}", operation_dict) for s in sequences]
if upload:
ps.Pulsar.get_instance().program_awgs(sequences[0])
return sequences, np.arange(sequences[0].n_acq_elements()), \
np.arange(len(cliffords))
def Randomized_Benchmarking_seq(pulse_pars,
RO_pars,
nr_cliffords,
nr_seeds,
net_clifford=0,
post_msmt_delay=3e-6,
cal_points=True,
resetless=False,
double_curves=False,
seq_name=None,
verbose=False,
upload=True):
'''
Input pars:
pulse_pars: dict containing pulse pars
RO_pars: dict containing RO pars
nr_cliffords: list nr_cliffords for which to generate RB seqs
nr_seeds: int nr_seeds for which to generate RB seqs
net_clifford: int index of net clifford the sequence should perform
0 corresponds to Identity and 3 corresponds to X180
post_msmt_delay:
cal_points: bool whether to replace the last two elements with
calibration points, set to False if you want
to measure a single element (for e.g. optimization)
resetless: bool if False will append extra Id element if seq
is longer than 50us to ensure proper initialization
double_curves: Alternates between net clifford 0 and 3
upload: Upload to the AWG
returns:
seq, elements_list
Conventional use:
nr_cliffords = [n1, n2, n3 ....]
cal_points = True
Optimization use (resetless or not):
nr_cliffords = [n] is a list with a single entry
cal_points = False
net_clifford = 3 (optional) make sure it does a net pi-pulse
post_msmt_delay is set (optional)
resetless (optional)
'''
if seq_name is None:
seq_name = 'RandomizedBenchmarking_sequence'
seq = sequence.Sequence(seq_name)
el_list = []
pulses = get_pulse_dict_from_pars(pulse_pars)
net_cliffords = [0, 3] # Exists purely for the double curves mode
i = 0
for seed in range(nr_seeds):
for j, n_cl in enumerate(nr_cliffords):
if double_curves:
net_clifford = net_cliffords[i % 2]
i += 1 # only used for ensuring unique elt names
if cal_points and (j == (len(nr_cliffords) - 4)
or j == (len(nr_cliffords) - 3)):
el = multi_pulse_elt(i, station, [pulses['I'], RO_pars])
elif cal_points and (j == (len(nr_cliffords) - 2)
or j == (len(nr_cliffords) - 1)):
el = multi_pulse_elt(i, station, [pulses['X180'], RO_pars])
else:
cl_seq = rb.randomized_benchmarking_sequence(
n_cl, desired_net_cl=net_clifford)
pulse_keys = rb.decompose_clifford_seq(cl_seq)
pulse_list = [pulses[x] for x in pulse_keys]
pulse_list += [RO_pars]
# copy first element and set extra wait
pulse_list[0] = deepcopy(pulse_list[0])
pulse_list[0]['pulse_delay'] += post_msmt_delay
el = multi_pulse_elt(i, station, pulse_list)
el_list.append(el)
seq.append_element(el, trigger_wait=True)
# If the element is too long, add in an extra wait elt
# to skip a trigger
if resetless and n_cl * pulse_pars['pulse_delay'] * 1.875 > 50e-6:
el = multi_pulse_elt(i, station, [pulses['I']])
el_list.append(el)
seq.append_element(el, trigger_wait=True)
if upload:
station.pulsar.program_awgs(seq, *el_list, verbose=verbose)
return seq, el_list
else:
return seq, el_list