def test(nphotons, nmodes, accelerate, explicit, mode='quantum'):
'''test the simulator'''
# build a basis, device and simulator
basis=lo.basis(nphotons, nmodes)
device=lo.random_unitary(basis.nmodes)
simulator=lo.simulator(basis, device)
simulator.configure_perm(accelerate, explicit)
mode='quantum' if not mode else 'classical'
simulator.set_mode(mode)
# put photons in the top modes
state=basis.get_state(['m'] + range(basis.nphotons))
#print str(state).strip()
simulator.set_input_state(state)
# how long does it take to work over the full hilbert space
t=clock()
for i in range(basis.hilbert_space_dimension):
simulator.get_probability(i)
util.progress_bar(i/float(basis.hilbert_space_dimension-1))
hilbert_space_time=clock()-t
return hilbert_space_time
def new_sample(device):
#phases=[0,pi,0,0,0,0,0,0]
#phases=[pi,pi,0,0,0,0,0,0]
phases=np.random.uniform(0,np.pi*2,8)
phases[6]=0.93361981
phases[0]=np.pi
counts=do_measurement(phases, ontime=2)
coincidences=np.array(counts[8:12])
accidentals=np.array([counts[12], counts[17], counts[16], counts[13]])
corrected_counts=coincidences-accidentals
probabilities_expt=corrected_counts/float(np.sum(corrected_counts))
print probabilities_expt
'''get some simulated probabilities'''
device.set_phases(phases)
simulator=lo.simulator(device, nphotons=2)
simulator.set_input_state([1,3])
probabilities_theory=simulator.get_probabilities(patterns=[[1,3],[1,4],[2,3],[2,4]])
sum_probs=sum(probabilities_theory)
probabilities_theory=np.array(probabilities_theory)/sum_probs
fidelity=statistical_fidelity(probabilities_expt, probabilities_theory)
return np.sum(coincidences), np.sum(accidentals), np.sum(fidelity), phases, coincidences, accidentals
def new_sample(device):
#phases=[0,pi,0,0,0,0,0,0]
#phases=[pi,pi,0,0,0,0,0,0]
phases = np.random.uniform(0, np.pi * 2, 8)
phases[6] = 0.93361981
phases[0] = np.pi
counts = do_measurement(phases, ontime=2)
coincidences = np.array(counts[8:12])
accidentals = np.array([counts[12], counts[17], counts[16], counts[13]])
corrected_counts = coincidences - accidentals
probabilities_expt = corrected_counts / float(np.sum(corrected_counts))
print probabilities_expt
'''get some simulated probabilities'''
device.set_phases(phases)
simulator = lo.simulator(device, nphotons=2)
simulator.set_input_state([1, 3])
probabilities_theory = simulator.get_probabilities(
patterns=[[1, 3], [1, 4], [2, 3], [2, 4]])
sum_probs = sum(probabilities_theory)
probabilities_theory = np.array(probabilities_theory) / sum_probs
fidelity = statistical_fidelity(probabilities_expt, probabilities_theory)
return np.sum(coincidences), np.sum(accidentals), np.sum(
fidelity), phases, coincidences, accidentals
#data=data/np.amax(data)
experiment_filename, theory_filename, param_filename, hold_table_filename = get_filenames(
heater_index)
file = open(hold_table_filename, "w")
file.write(str(hold_table))
file.close()
np.save(experiment_filename, experiment)
np.save(theory_filename, theory)
np.save(param_filename, parameter_space)
return experiment_filename, theory_filename, param_filename
start_time = timestamp()
device = lo.beamsplitter_network(json='cnot_mz.json')
simulator = lo.simulator(device, nphotons=2)
simulator.set_input_state([1, 3])
simulator.set_visibility(0.99)
dac = dac.dac()
fpga = fpga()
fpga.read()
fpga.read()
fpga.read()
fpga.read()
table = calibration_table()
heater_index = 6
#hold_table=[[0,pi/2],[2,0], [6,pi/2]]
hold_table = list(enumerate([pi, 0, 0, 0, 0, 0, 0, 0]))
# Take data
# Close hardware
dac.zero()
# Normalize, save and return data
#data=data/np.amax(data)
experiment_filename, theory_filename, param_filename = get_filenames(heater_index)
np.save(experiment_filename, experiment)
np.save(theory_filename, theory)
np.save(param_filename, parameter_space)
return experiment_filename, theory_filename, param_filename
start_time=timestamp()
device=lo.beamsplitter_network(json='cnot_mz.json')
simulator=lo.simulator(device, nphotons=2)
simulator.set_input_state([1,3])
simulator.set_visibility(0.95)
dac=dac.dac()
fpga=fpga()
fpga.read()
fpga.read()
fpga.read()
fpga.read()
table=calibration_table()
heater_index = 5
#hold_table=[[0,pi/2],[2,0], [6,pi/2]]
hold_table=list(enumerate([0,pi/2,0,0,0,0,0,pi/2]))
# Take data
import numpy as np
from qy.simulation import linear_optics as lo
'''
Test our code with the CNOT-MZ
'''
# Build the device and a two-photon basis, draw the device
basis=lo.basis(2,2)
device=lo.beamsplitter_network(json='devices/cnot_mz.json')
device.draw('devices/cnot_mz.pdf')
# Start a simulator
simulator=lo.simulator(device, basis)
# Set the input state and get some probabilities
input_state=basis.get_state([1,3])
simulator.set_input_state(input_state)
print 'INPUT:', input_state
print 'P00', simulator.get_probability_quantum([1,3])
print 'P01', simulator.get_probability_quantum([2,3])
print 'P10', simulator.get_probability_quantum([1,4])
print 'P11', simulator.get_probability_quantum([2,4])
# Let's try a few different input states
for input_modes in [[0,1], [0,0], [1,1]]:
input_state=basis.get_state(input_modes)
simulator.set_input_state(input_state)
print '\nINPUT:', str(input_state).strip()
for index, modes in basis:
q=simulator.get_probability_quantum(index)
import numpy as np
from qy.simulation import linear_optics as lo
'''
Test our code with the CNOT-MZ
'''
# Build the device and a two-photon basis, draw the device
basis = lo.basis(2, 2)
device = lo.beamsplitter_network(json='devices/cnot_mz.json')
device.draw('devices/cnot_mz.pdf')
# Start a simulator
simulator = lo.simulator(device, basis)
# Set the input state and get some probabilities
input_state = basis.get_state([1, 3])
simulator.set_input_state(input_state)
print 'INPUT:', input_state
print 'P00', simulator.get_probability_quantum([1, 3])
print 'P01', simulator.get_probability_quantum([2, 3])
print 'P10', simulator.get_probability_quantum([1, 4])
print 'P11', simulator.get_probability_quantum([2, 4])
# Let's try a few different input states
for input_modes in [[0, 1], [0, 0], [1, 1]]:
input_state = basis.get_state(input_modes)
simulator.set_input_state(input_state)
print '\nINPUT:', str(input_state).strip()
for index, modes in basis:
q = simulator.get_probability_quantum(index)
c = simulator.get_probability_classical(index)
