def test_StateTomo_detailed():
NumberOfIons = 2
PhononOverhead = 1
hspace = sim.hspace(NumberOfIons, 2, NumberOfIons + PhononOverhead, 0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('SS,0', hspace)] = 1
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, 2*pi, 0, 0), \
] )
ScanObj = IPS.ScanParameter_in_Sequence(pulseseq,
params,
dec,
np.arange(3**NumberOfIons),
type='StateTomo')
ScanObj.runScan()
data_dmr = ScanObj.output_dict['qstates_camera']
rho = dmr.IterML.iterfun(data_dmr, 100)
#if np.real(rho[3,3]) > 0.99:
# print 'statetomo passed'
return np.real(rho[3, 3]) > 0.99
def test_ProcTomo_detailed():
NumberOfIons = 1
PhononOverhead = 7
hspace = sim.hspace(NumberOfIons, 2, NumberOfIons + PhononOverhead, 0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('S,0', hspace)] = 1
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2,0),
] )
ScanObj = IPS.ScanParameter_in_Sequence(pulseseq,
params,
dec,
np.arange(12**NumberOfIons),
type='ProcTomo')
ScanObj.runScan()
data_proctom = ScanObj.output_dict['qstates_camera']
chi = proctom.proctomo(data_proctom, 100)
#if np.real(chi[0,0]) > 0.99:
# print 'proctomo passed'
chiId = qproc.Unitary2Chi(pulseseq[0].Uidtr.conjugate())
return np.max(abs(chi - chiId)) < 0.001
def test_Rabi_carrier_detailed(figNum):
#print(TestUserFUnction.figNum, TestUserFunction.figNum_start, "<<<<")
NumberOfIons = 1
PhononOverhead = 2
hspace = sim.hspace(NumberOfIons,2,NumberOfIons+PhononOverhead,0)
params = sim.parameters(hspace)
params.stepsize = 1
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('S,0', hspace)] = 1
params.printpulse = False # don't print pulse details
pulseseq = sim.PulseSequence( [
sim.Rcar(params, 10*pi, 0, -1)
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
# start with fit here
x = data.T
y = data.YtrN.transpose()[0] # this is the s-state population
# p[0] ... amplitude, should be 1
# p[1] ... freq, should be params.omc
# p[2] ... phase, should be 0
# p[3] ... offset, should be 0
startparams = np.array([1, params.omc, 0, 0])
# 1-data ... to get the D-state population
fitfun = lambda p, x: 1-p[0]*np.sin(p[1]*x/2+p[2])**2 + p[3]
errfun = lambda p, x, y: y-fitfun(p,x)
par, covmat, infodict, mesg, ier = leastsq(errfun, startparams, args=(x,y), full_output = True)
#print(startparams)
#print(par)
#print(startparams-par)
epsilon = 10**-5
if par[0] - startparams[0] > epsilon:
print("amplitude of Rabi oscillations wrong")
if par[1] - startparams[1] > epsilon:
print("frequency of Rabi oscillations wrong")
if par[2] - startparams[2] > epsilon:
print("phase of Rabi oscillations wrong")
if par[3] - startparams[3] > epsilon:
print("offset of Rabi oscillations wrong")
return np.all(par-startparams < epsilon)
def test_Rabi_carrier_detailed(figNum):
#print TestUserFUnction.figNum, TestUserFunction.figNum_start, "<<<<"
NumberOfIons = 1
PhononOverhead = 2
hspace = sim.hspace(NumberOfIons,2,NumberOfIons+PhononOverhead,0)
params = sim.parameters(hspace)
params.stepsize = 1
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('S,0', hspace)] = 1
params.printpulse = False # don't print pulse details
pulseseq = sim.PulseSequence( [
sim.Rcar(params, 10*pi, 0, -1)
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
# start with fit here
x = data.T
y = data.YtrN.transpose()[0] # this is the s-state population
# p[0] ... amplitude, should be 1
# p[1] ... freq, should be params.omc
# p[2] ... phase, should be 0
# p[3] ... offset, should be 0
startparams = np.array([1, params.omc, 0, 0])
# 1-data ... to get the D-state population
fitfun = lambda p, x: 1-p[0]*np.sin(p[1]*x/2+p[2])**2 + p[3]
errfun = lambda p, x, y: y-fitfun(p,x)
par, covmat, infodict, mesg, ier = leastsq(errfun, startparams, args=(x,y), full_output = True)
#print startparams
#print par
#print startparams-par
epsilon = 10**-5
if par[0] - startparams[0] > epsilon:
print "amplitude of Rabi oscillations wrong"
if par[1] - startparams[1] > epsilon:
print "frequency of Rabi oscillations wrong"
if par[2] - startparams[2] > epsilon:
print "phase of Rabi oscillations wrong"
if par[3] - startparams[3] > epsilon:
print "offset of Rabi oscillations wrong"
return np.all(par-startparams < epsilon)
def test_spontdecay_detailed(figNum):
NumberOfIons = 1
NumberofPhonons = 1
hspace = sim.hspace(NumberOfIons, 2, NumberofPhonons, 0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
dec.doRandNtimes = 100
dec.dict['spontdecay'] = True
dec.doPP = True
dec.doPPprintstats = False
dec.progbar = False
# for the test we set it to 300 mus, instead of 1.168 s
params.lifetime = 300
params.y0[qc.stateToIndex(NumberOfIons * 'D' + ',0', hspace)] = 1
params.y0[qc.stateToIndex(NumberOfIons * 'S' + ',0', hspace)] = 0
pulseseq = sim.PulseSequence( [ \
sim.Delay(params, 1000), \
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
# fitting part
p0 = [1, params.lifetime]
fitfun = lambda p, x: p[0] * np.exp(-x / float(p[1]))
errfun = lambda p, x, y: y - fitfun(p, x)
x = data.T
y = data.YtrN[:, 0]
par, covmat, infodict, mesg, ier = leastsq(errfun,
p0,
args=(x, y),
full_output=True)
epsilon = 50 # with 100 iterations allow 50us offset in decay time
# print(np.abs(par[-1] - params.lifetime))
return np.abs(par[-1] - params.lifetime) < epsilon
def test_spontdecay_detailed(figNum):
NumberOfIons = 1
NumberofPhonons = 1
hspace = sim.hspace(NumberOfIons,2,NumberofPhonons,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
dec.doRandNtimes = 100
dec.dict['spontdecay'] = True
dec.doPP = True
dec.doPPprintstats = False
dec.progbar = False
# for the test we set it to 300 mus, instead of 1.168 s
params.lifetime = 300
params.y0[qc.stateToIndex(NumberOfIons*'D'+',0', hspace)] = 1
params.y0[qc.stateToIndex(NumberOfIons*'S'+',0', hspace)] = 0
pulseseq = sim.PulseSequence( [ \
sim.Delay(params, 1000), \
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
# fitting part
p0 = [1, params.lifetime]
fitfun = lambda p, x: p[0] * np.exp(-x / float(p[1]))
errfun = lambda p, x, y: y-fitfun(p,x)
x = data.T
y = data.YtrN[:,0]
par, covmat, infodict, mesg, ier = leastsq(errfun, p0, args=(x,y), full_output = True)
epsilon = 50 # with 100 iterations allow 50us offset in decay time
# print np.abs(par[-1] - params.lifetime)
return np.abs(par[-1] - params.lifetime) < epsilon
def test_state_initialisation_detailed(figNum):
NumberOfIons = 1
NumberOfPhonons = 1
hspace = sim.hspace(NumberOfIons, 2, NumberOfPhonons, 0)
params = sim.parameters(hspace)
params.stateiniterr = 0.2
dec = sim.decoherence(params)
dec.doPP = False
dec.progbar = False
dec.doRandNtimes = 10
dec.dict['initerr'] = True
params.y0[qc.stateToIndex(NumberOfIons * 'S' + ',0', hspace)] = 1
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, 5*pi, 0),
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
# start with fit here
x = data.T
y = data.YtrN.transpose()[-1] # this is the d-state population
# p[0] ... amplitude, should be 0.8
# p[1] ... freq, should be params.omc
# p[2] ... phase, should be 0
# p[3] ... offset, should be 0
startparams = np.array([0.8, params.omc, 0, 0])
# 1-data ... to get the D-state population
fitfun = lambda p, x: p[0] * np.sin(p[1] * x / 2 + p[2])**2 + p[3]
errfun = lambda p, x, y: y - fitfun(p, x)
par, covmat, infodict, mesg, ier = leastsq(errfun,
startparams,
args=(x, y),
full_output=True)
# print(par)
# even for the 1000 realisations, allow for a 3% error
epsilon = 0.03
return abs(abs(par[0]) - startparams[0]) < epsilon
def test_state_initialisation_detailed(figNum):
NumberOfIons = 1
NumberOfPhonons = 1
hspace = sim.hspace(NumberOfIons,2,NumberOfPhonons,0)
params = sim.parameters(hspace)
params.stateiniterr = 0.2
dec = sim.decoherence(params)
dec.doRandNtimes = 1000
dec.dict['initerr'] = True
params.y0[qc.stateToIndex(NumberOfIons*'S'+',0', hspace)] = 1
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, 5*pi, 0),
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
# start with fit here
x = data.T
y = data.YtrN.transpose()[-1] # this is the d-state population
# p[0] ... amplitude, should be 0.8
# p[1] ... freq, should be params.omc
# p[2] ... phase, should be 0
# p[3] ... offset, should be 0
startparams = np.array([0.8, params.omc, 0, 0])
# 1-data ... to get the D-state population
fitfun = lambda p, x: p[0]*np.sin(p[1]*x/2+p[2])**2 + p[3]
errfun = lambda p, x, y: y-fitfun(p,x)
par, covmat, infodict, mesg, ier = leastsq(errfun, startparams, args=(x,y), full_output = True)
# print par
# even for the 1000 realisations, allow for a 3% error
epsilon = 0.03
return abs(abs(par[0])-startparams[0]) < epsilon
def test_StateTomo_detailed():
NumberOfIons = 2
PhononOverhead = 1
hspace = sim.hspace(NumberOfIons,2,NumberOfIons+PhononOverhead,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('SS,0', hspace)] = 1
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, 2*pi, 0, 0), \
] )
ScanObj = IPS.ScanParameter_in_Sequence(pulseseq, params, dec, np.arange(3**NumberOfIons), type = 'StateTomo')
ScanObj.runScan()
data_dmr = ScanObj.output_dict['qstates_camera']
rho = dmr.IterML.iterfun(data_dmr, 100)
#if np.real(rho[3,3]) > 0.99:
# print 'statetomo passed'
return np.real(rho[3,3]) > 0.99
def test_ProcTomo_detailed():
NumberOfIons = 1
PhononOverhead = 7
hspace = sim.hspace(NumberOfIons,2,NumberOfIons+PhononOverhead,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('S,0', hspace)] = 1
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2,0),
] )
ScanObj = IPS.ScanParameter_in_Sequence(pulseseq, params, dec, np.arange(12**NumberOfIons), type = 'ProcTomo')
ScanObj.runScan()
data_proctom = ScanObj.output_dict['qstates_camera']
chi = proctom.proctomo(data_proctom, 100)
#if np.real(chi[0,0]) > 0.99:
# print 'proctomo passed'
chiId = qproc.Unitary2Chi(pulseseq[0].Uidtr.conjugate())
return np.max(abs(chi - chiId)) < 0.001
''' Cirac-Zoller CNOT '''
import numpy as np
import PyTIQC.core.simtools as sim
import PyTIQC.core.qctools as qc
pi = np.pi
hspace = sim.hspace(2,2,2,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('SS,0', hspace)] = 1
params.stepsize = 10
params.ignorelightshift = 1
#params.addressing = np.array([[1,0.1],[0.1,1]])
ang = np.arccos(-np.real(np.exp(pi/2*1j*np.sqrt(2))));
pulseseq = sim.PulseSequence( [ \
sim.Rblue(params, pi, 0, 0), \
sim.Rcar(params, pi/2, 0, 1), \
sim.Rblue(params, pi/2, 0, 1), \
sim.Rblue(params, np.sqrt(2)*pi, pi/2, 1), \
sim.Rblue(params, pi/2, pi, 1), \
sim.Rcar(params, pi/2, pi + ang, 1), \
sim.Rblue(params, pi, 0, 0) \
] )
data = qc.simulateevolution(pulseseq, params, dec)
data.displaypopulation(1)
''' Cirac-Zoller CNOT '''
import PyTIQC
import numpy as np
import PyTIQC.core.simtools as sim
import PyTIQC.core.qctools as qc
pi = np.pi
hspace = sim.hspace(2, 2, 2, 0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('SS,0', hspace)] = 1
params.stepsize = 10
params.ignorelightshift = 1
#params.addressing = np.array([[1,0.1],[0.1,1]])
ang = np.arccos(-np.real(np.exp(pi / 2 * 1j * np.sqrt(2))))
pulseseq = sim.PulseSequence( [ \
sim.Rblue(params, pi, 0, 0), \
sim.Rcar(params, pi/2, 0, 1), \
sim.Rblue(params, pi/2, 0, 1), \
sim.Rblue(params, np.sqrt(2)*pi, pi/2, 1), \
sim.Rblue(params, pi/2, pi, 1), \
sim.Rcar(params, pi/2, pi + ang, 1), \
sim.Rblue(params, pi, 0, 0) \
] )
data = qc.simulateevolution(pulseseq, params, dec)
def test_Ramsey_carrier_detailed(figNum):
NumberOfIons = 1
PhononOverhead = 2
hspace = sim.hspace(NumberOfIons,2,NumberOfIons+PhononOverhead,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('S,0', hspace)] = 1
params.stepsize = 1
params.printpulse = False # don't print pulse details
numberofpoints = 20
phi = np.linspace(0, 2*pi, numberofpoints)
ex = np.zeros(numberofpoints)
for i in range(numberofpoints):
pulseseq = sim.PulseSequence( [
sim.Rcar(params, pi/2, 0, -1),
# sim.Delay(params, tdelay[i]),
sim.Rcar(params, pi/2, phi[i], -1)
])
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
ex[i] = data.YtrN[-1,0]
# fig1 = pl.figure(1)
# ax1 = fig1.add_subplot(111)
# ax1.plot(phi, ex)
# fig1.show()
# p[0] ... amplitude, should be 1
# p[1] ... because phase is in units of pi -> 1
# p[2] ... phase, should be 0
# p[3] ... offset, should be 0.5
startparams = np.array([1, 1, 0, 0.5])
# 1-data ... to get the D-state population
fitfun = lambda p, x: p[0]/2*np.cos(p[1]*x+p[2]) + p[3]
errfun = lambda p, x, y: y-fitfun(p,x)
par, covmat, infodict, mesg, ier = leastsq(errfun, startparams, args=(phi,ex), full_output = True)
#print(startparams)
#print(par)
#print(startparams-par)
epsilon = 10**-5
if par[0] - startparams[0] > epsilon:
print("amplitude of Ramsey experiment wrong")
if par[1] - startparams[1] > epsilon:
print("frequency of Ramsey experiment wrong")
if par[2] - startparams[2] > epsilon:
print("phase of Ramsey experiment wrong")
if par[3] - startparams[3] > epsilon:
print("offset of Ramsey experiment wrong")
return np.all(par-startparams < epsilon)
def test_Ramsey_carrier_detailed(figNum):
NumberOfIons = 1
PhononOverhead = 2
hspace = sim.hspace(NumberOfIons,2,NumberOfIons+PhononOverhead,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.y0[qc.stateToIndex('S,0', hspace)] = 1
params.stepsize = 1
params.printpulse = False # don't print pulse details
numberofpoints = 20
phi = np.linspace(0, 2*pi, numberofpoints)
ex = np.zeros(numberofpoints)
for i in range(numberofpoints):
pulseseq = sim.PulseSequence( [
sim.Rcar(params, pi/2, 0, -1),
# sim.Delay(params, tdelay[i]),
sim.Rcar(params, pi/2, phi[i], -1)
])
data = qc.simulateevolution(pulseseq, params, dec)
data.tracedpopulation(figNum)
ex[i] = data.YtrN[-1,0]
# fig1 = pl.figure(1)
# ax1 = fig1.add_subplot(111)
# ax1.plot(phi, ex)
# fig1.show()
# p[0] ... amplitude, should be 1
# p[1] ... because phase is in units of pi -> 1
# p[2] ... phase, should be 0
# p[3] ... offset, should be 0.5
startparams = np.array([1, 1, 0, 0.5])
# 1-data ... to get the D-state population
fitfun = lambda p, x: p[0]/2*np.cos(p[1]*x+p[2]) + p[3]
errfun = lambda p, x, y: y-fitfun(p,x)
par, covmat, infodict, mesg, ier = leastsq(errfun, startparams, args=(phi,ex), full_output = True)
#print startparams
#print par
#print startparams-par
epsilon = 10**-5
if par[0] - startparams[0] > epsilon:
print "amplitude of Ramsey experiment wrong"
if par[1] - startparams[1] > epsilon:
print "frequency of Ramsey experiment wrong"
if par[2] - startparams[2] > epsilon:
print "phase of Ramsey experiment wrong"
if par[3] - startparams[3] > epsilon:
print "offset of Ramsey experiment wrong"
return np.all(par-startparams < epsilon)
