def flipping(qubit_idx: int,
number_of_flips,
platf_cfg: str,
equator: bool = False,
cal_points: bool = True):
"""
Generates a flipping sequence that performs multiple pi-pulses
Basic sequence:
- (X)^n - RO
Input pars:
qubit_idx: int specifying the target qubit (starting at 0)
number_of_flips: array of ints specifying the sweep points
platf_cfg: filename of the platform config file
equator: if True add an extra pi/2 pulse at the end to
make the state end at the equator.
cal_points: replaces last 4 points by calibration points
Returns:
p: OpenQL Program object
"""
platf = Platform('OpenQL_Platform', platf_cfg)
p = Program(pname="Flipping", nqubits=platf.get_qubit_number(), p=platf)
for i, n in enumerate(number_of_flips):
k = Kernel("Flipping_" + str(i), p=platf)
k.prepz(qubit_idx)
if cal_points and (i == (len(number_of_flips) - 4)
or i == (len(number_of_flips) - 3)):
k.measure(qubit_idx)
elif cal_points and (i == (len(number_of_flips) - 2)
or i == (len(number_of_flips) - 1)):
k.x(qubit_idx)
k.measure(qubit_idx)
else:
if equator:
k.gate('rx90', qubit_idx)
for j in range(n):
k.x(qubit_idx)
k.measure(qubit_idx)
p.add_kernel(k)
with suppress_stdout():
p.compile(verbose=False)
# attribute get's added to program to help finding the output files
p.output_dir = ql.get_output_dir()
p.filename = join(p.output_dir, p.name + '.qisa')
return p
def single_elt_on(qubit_idx: int, platf_cfg: str):
platf = Platform('OpenQL_Platform', platf_cfg)
p = Program(pname="Single_elt_on",
nqubits=platf.get_qubit_number(),
p=platf)
k = Kernel("main", p=platf)
k.prepz(qubit_idx)
k.x(qubit_idx)
k.measure(qubit_idx)
p.add_kernel(k)
with suppress_stdout():
p.compile(verbose=False)
# attribute get's added to program to help finding the output files
p.output_dir = ql.get_output_dir()
p.filename = join(p.output_dir, p.name + '.qisa')
return p
def butterfly(qubit_idx: int, initialize: bool, platf_cfg: str):
"""
Performs a 'butterfly' sequence on the qubit specified.
0: prepz (RO) - - RO - RO
1: prepz (RO) - x180 - RO - RO
Args:
qubit_idx (int) : index of the qubit
initialize (bool): if True does an extra initial measurement to
post select data.
platf_cfg (str) : openql config used for setup.
"""
platf = Platform('OpenQL_Platform', platf_cfg)
p = Program(pname="Butterfly", nqubits=platf.get_qubit_number(), p=platf)
k = Kernel('0', p=platf)
k.prepz(qubit_idx)
if initialize:
k.measure(qubit_idx)
k.measure(qubit_idx)
k.measure(qubit_idx)
p.add_kernel(k)
k = Kernel('1', p=platf)
k.prepz(qubit_idx)
if initialize:
k.measure(qubit_idx)
k.x(qubit_idx)
k.measure(qubit_idx)
k.measure(qubit_idx)
p.add_kernel(k)
with suppress_stdout():
p.compile(verbose=False)
# attribute get's added to program to help finding the output files
p.output_dir = ql.get_output_dir()
p.filename = join(p.output_dir, p.name + '.qisa')
return p
def randomized_benchmarking(qubit_idx: int,
platf_cfg: str,
nr_cliffords,
nr_seeds: int,
net_clifford: int = 0,
restless: bool = False,
program_name: str = 'randomized_benchmarking',
cal_points: bool = True,
double_curves: bool = False):
'''
Input pars:
qubit_idx: int specifying the target qubit (starting at 0)
platf_cfg: filename of the platform config file
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 -> Idx
3 -> rx180
restless: bool, does not initialize if restless is True
program_name: 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:
p: OpenQL Program object
generates a program for single qubit Clifford based randomized
benchmarking.
'''
net_cliffords = [0, 3] # Exists purely for the double curves mode
platf = Platform('OpenQL_Platform', platf_cfg)
p = Program(pname=program_name, nqubits=platf.get_qubit_number(), p=platf)
i = 0
for seed in range(nr_seeds):
for j, n_cl in enumerate(nr_cliffords):
k = Kernel('RB_{}Cl_s{}'.format(n_cl, seed), p=platf)
if not restless:
k.prepz(qubit_idx)
if cal_points and (j == (len(nr_cliffords) - 4)
or j == (len(nr_cliffords) - 3)):
k.measure(qubit_idx)
elif cal_points and (j == (len(nr_cliffords) - 2)
or j == (len(nr_cliffords) - 1)):
k.x(qubit_idx)
k.measure(qubit_idx)
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 cl in cl_seq:
k.gate('cl_{}'.format(cl), qubit_idx)
k.measure(qubit_idx)
p.add_kernel(k)
with suppress_stdout():
p.compile(verbose=False)
# attribute get's added to program to help finding the output files
p.output_dir = ql.get_output_dir()
p.filename = join(p.output_dir, p.name + '.qisa')
return p
