def test_is_unitary_with_channel(self, prog):
"""test that the is_unitary function returns False if channels are present"""
with prog.context as q:
Sgate(0.4) | q[0]
LossChannel(0.4) | q[0]
BSgate(0.4) | q
assert not utils.is_unitary(prog)
def test_is_channel_no_measurement(self, prog):
"""test that the is_channel function returns True if no measurements are present"""
assert utils.is_channel(prog)
with prog.context as q:
Sgate(0.4) | q[0]
LossChannel(0.4) | q[0]
BSgate(0.4) | q
assert utils.is_channel(prog)
def test_is_unitary_with_channel(self):
"""test that the is_unitary function returns False if channels are present"""
eng, q = sf.Engine(2)
with eng:
Sgate(0.4) | q[0]
LossChannel(0.4) | q[0]
BSgate(0.4) | q
assert not utils.is_unitary(eng)
def test_is_channel_no_measurement(self):
"""test that the is_channel function returns True if no measurements are present"""
eng, q = sf.Engine(2)
assert utils.is_channel(eng)
with eng:
Sgate(0.4) | q[0]
BSgate(0.4) | q
assert utils.is_channel(eng)
with eng:
Sgate(0.4) | q[0]
LossChannel(0.4) | q[0]
BSgate(0.4) | q
assert utils.is_channel(eng)
etas_loop = device.certificate["loop_efficiencies"]
# a list with the relative efficiencies of the 16 detector channels
etas_ch_rel = device.certificate["relative_channel_efficiencies"]
# the detector-channel efficiencies are cyclic, so mode ``i`` sees the same
# efficiency as mode ``i+1``
prog_length = len(gate_args_list[0])
reps = int(np.ceil(prog_length / 16))
etas_ch_rel = np.tile(etas_ch_rel, reps)[:prog_length]
# create a Strawberry Fields program for a local simulation
prog_sim = sf.TDMProgram(N)
with prog_sim.context(*gate_args_list, etas_ch_rel) as (p, q):
Sgate(p[0]) | q[n[0]]
LossChannel(eta_glob) | q[n[0]]
for i in range(len(delays)):
Rgate(p[2 * i + 1]) | q[n[i]]
BSgate(p[2 * i + 2], np.pi / 2) | (q[n[i + 1]], q[n[i]])
LossChannel(etas_loop[i]) | q[n[i]]
LossChannel(p[7]) | q[0]
MeasureFock() | q[0]
prog_sim.space_unroll()
# create Gaussian engine and submit program to it
eng_sim = sf.Engine(backend="gaussian")
results_sim = eng_sim.run(prog_sim, shots=None, crop=True)
# obtain quadrature covariance matrix
cov = results_sim.state.cov()