def doRamseyRun(hspace, params, dec):
tdelay = np.linspace(0, 5000, 500)
YR = np.zeros([len(tdelay), hspace.dimensions], np.complex64)
pop = np.zeros([len(tdelay), hspace.dimensions], np.complex64)
widgets = [progbar.Percentage(), " ", progbar.Bar(), " ", progbar.ETA()]
pbar = progbar.ProgressBar(widgets=widgets).start()
for i in range(len(tdelay)):
pulseseq = sim.PulseSequence(
[sim.Rcar(params, pi / 2, 0), sim.Delay(params, tdelay[i]), sim.Rcar(params, pi / 2, pi / 2)]
)
data = qc.simulateevolution(pulseseq, params, dec)
YR[i, :] = data.YR[-1, :]
pbar.update(int(1.0 * (i + 1) * 100 / (len(tdelay))))
data1 = sim.database(tdelay, YR, hspace, pulseseq=None, statesvalid=False)
data1.tracedpopulation(0)
[fitfun, par] = doRamseyFit(data1)
return data1, fitfun, par
def doRamseyRun(hspace, params, dec):
tdelay = np.linspace(0,5000,500)
YR = np.zeros([len(tdelay),hspace.dimensions], np.complex64)
pop = np.zeros([len(tdelay),hspace.dimensions], np.complex64)
widgets = [progbar.Percentage(), ' ', progbar.Bar(),' ', progbar.ETA()]
pbar = progbar.ProgressBar(widgets=widgets).start()
for i in range(len(tdelay)):
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2, 0), \
sim.Delay(params, tdelay[i]), \
sim.Rcar(params, pi/2, pi/2) \
] )
data = qc.simulateevolution(pulseseq, params, dec)
YR[i,:] = data.YR[-1,:]
pbar.update(int(1.*(i+1)*100/(len(tdelay))))
data1 = sim.database(tdelay, YR, hspace, pulseseq=None, statesvalid = False)
data1.tracedpopulation(0)
[fitfun, par] = doRamseyFit(data1)
return data1, fitfun, par
def test_dephasing_detailed(figNum):
NumberOfIons = 1
NumberofPhonons = 0
hspace = sim.hspace(NumberOfIons, 2, NumberofPhonons, 0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.coherenceTime = 1000
params.correlationTime = 0.1
dec.doRandNtimes = 20
dec.dict['dephase'] = True
dec.progbar = False
params.stepsize = 10
params.ODEtimestep = 1
params.detuning = 2 * pi * 0.01 #kHz
params.progbar = False
#dec.doPP = True
#params.use_servers(['local'])
#dec.doPPprintstats = False
tdelay = np.linspace(0, 1000, 100)
YR = np.zeros([len(tdelay), hspace.dimensions], np.complex64)
pop = np.zeros([len(tdelay), hspace.dimensions], np.complex64)
widgets = [progbar.Percentage(), ' ', progbar.Bar(), ' ', progbar.ETA()]
pbar = progbar.ProgressBar(widgets=widgets).start()
for i in range(len(tdelay)):
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2, 0), \
sim.Delay(params, tdelay[i]), \
sim.Rcar(params, pi/2, pi/2) \
] )
data = qc.simulateevolution(pulseseq, params, dec)
YR[i, :] = data.YR[-1, :]
pbar.update(int(1. * (i + 1) * 100 / (len(tdelay))))
data1 = sim.database(tdelay, YR, hspace, pulseseq=None, statesvalid=False)
data1.tracedpopulation(figNum)
# fitting part
p0 = [0.5, params.detuning, pi / 2, 0.5, params.coherenceTime]
fitfun = lambda p, x: p[0] * np.cos(p[1] * x + p[2]) * np.exp(-np.log(
2) * (x / p[4])**2) + p[3]
errfun = lambda p, x, y: y - fitfun(p, x)
x = data1.T
y = data1.YR[:, 0]
par, covmat, infodict, mesg, ier = leastsq(errfun,
p0,
args=(x, y),
full_output=True)
epsilon = 100 # with 20 iterations allow 100us offset in coherence time
#print(par)
return data1
def test_ACStark_detailed(figNum):
NumberOfIons = 1
hspace = sim.hspace(NumberOfIons,2,0,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.progbar = False
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2, 0),
sim.Rac(params, 0, 0, 0),
sim.Rcar(params, pi/2, 0)
] )
ACtime = np.linspace(0,10*pi,100)
realtime = np.zeros_like(ACtime)
YR = np.zeros([len(ACtime), hspace.dimensions], dtype='complex')
for i in range(len(ACtime)):
pulseseq[1] = sim.Rac(params, ACtime[i], 0, 0)
realtime[i] = pulseseq[1].duration
data = qc.simulateevolution(pulseseq, params, dec)
YR[i,:] = data.YR[-1,:]
data1 = sim.database(realtime, YR, hspace, statesvalid = False)
data1.tracedpopulation(figNum)
# start with fit here
x = data1.T
y = data1.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.omac, 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)
epsilon = 10**-3
if par[0] - startparams[0] > epsilon:
print("amplitude of AC oscillations wrong")
if par[1] - startparams[1] > epsilon:
print("frequency of AC oscillations wrong")
if par[2] - startparams[2] > epsilon:
print("phase of AC oscillations wrong")
if par[3] - startparams[3] > epsilon:
print("offset of AC oscillations wrong")
return np.all(par-startparams < epsilon), data1, fitfun, par, startparams
def test_ACStark_detailed(figNum):
NumberOfIons = 1
hspace = sim.hspace(NumberOfIons,2,0,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.progbar = False
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2, 0),
sim.Rac(params, 0, 0, 0),
sim.Rcar(params, pi/2, 0)
] )
ACtime = np.linspace(0,10*pi,100)
realtime = np.zeros_like(ACtime)
YR = np.zeros([len(ACtime), hspace.dimensions], dtype='complex')
for i in range(len(ACtime)):
pulseseq[1] = sim.Rac(params, ACtime[i], 0, 0)
realtime[i] = pulseseq[1].duration
data = qc.simulateevolution(pulseseq, params, dec)
YR[i,:] = data.YR[-1,:]
data1 = sim.database(realtime, YR, hspace, statesvalid = False)
data1.tracedpopulation(figNum)
# start with fit here
x = data1.T
y = data1.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.omac, 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)
epsilon = 10**-3
if par[0] - startparams[0] > epsilon:
print "amplitude of AC oscillations wrong"
if par[1] - startparams[1] > epsilon:
print "frequency of AC oscillations wrong"
if par[2] - startparams[2] > epsilon:
print "phase of AC oscillations wrong"
if par[3] - startparams[3] > epsilon:
print "offset of AC oscillations wrong"
return np.all(par-startparams < epsilon), data1, fitfun, par, startparams
def test_dephasing_detailed(figNum):
NumberOfIons = 1
NumberofPhonons = 0
hspace = sim.hspace(NumberOfIons,2,NumberofPhonons,0)
params = sim.parameters(hspace)
dec = sim.decoherence(params)
params.coherenceTime = 1000
params.correlationTime = 0.1
dec.doRandNtimes = 20
dec.dict['dephase'] = True
dec.progbar = False
params.stepsize = 10
params.ODEtimestep = 1
params.detuning = 2*pi*0.01 #kHz
params.progbar = False
#dec.doPP = True
#params.use_servers(['local'])
#dec.doPPprintstats = False
tdelay = np.linspace(0,1000,100)
YR = np.zeros([len(tdelay),hspace.dimensions], np.complex64)
pop = np.zeros([len(tdelay),hspace.dimensions], np.complex64)
widgets = [progbar.Percentage(), ' ', progbar.Bar(),' ', progbar.ETA()]
pbar = progbar.ProgressBar(widgets=widgets).start()
for i in range(len(tdelay)):
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/2, 0), \
sim.Delay(params, tdelay[i]), \
sim.Rcar(params, pi/2, pi/2) \
] )
data = qc.simulateevolution(pulseseq, params, dec)
YR[i,:] = data.YR[-1,:]
pbar.update(int(1.*(i+1)*100/(len(tdelay))))
data1 = sim.database(tdelay, YR, hspace, pulseseq=None, statesvalid = False)
data1.tracedpopulation(figNum)
# fitting part
p0 = [0.5, params.detuning, pi/2, 0.5, params.coherenceTime]
fitfun = lambda p, x: p[0] * np.cos(p[1]*x+p[2]) * np.exp(-np.log(2)*(x/p[4])**2) + p[3]
errfun = lambda p, x, y: y-fitfun(p,x)
x = data1.T
y = data1.YR[:,0]
par, covmat, infodict, mesg, ier = leastsq(errfun, p0, args=(x,y), full_output = True)
epsilon = 100 # with 20 iterations allow 100us offset in coherence time
#print par
return data1
def loadsimdata(self, data, print_fidelities=False):
''' load simulation data directly from variables '''
try: # data is a database object
self.data = data
self.data.RhoPNAll = np.array(self.data.RhoPNAll)
self.rhosim = self.data.RhoPN
except AttributeError: # or it's just the densmats
# make a dummy data object
self.data = sim.database(np.zeros(1), np.zeros(1), sim.hspace(1,2,0,0))
self.data.RhoPNAll = data
self.simloaded = True
if print_fidelities:
for i in range(np.min([len(self.rhosim), len(self.rho)])):
print i, ": ", U.jozsafid(self.rho[i], self.rhosim[i])
dec = sim.decoherence(params)
params.stepsize = 600
detuning = np.arange(-1.2 * params.omz, 1.2 * params.omz, params.omz / 300)
# dets1 = np.arange(-1.1*params.omz, -0.9*params.omz, 2*pi*0.001)
# detc = np.arange(-2*pi*0.1, 2*pi*0.1, 2*pi*0.001)
# dets2 = np.arange(0.9*params.omz, 1.1*params.omz, 2*pi*0.001)
# detuning = np.hstack([ dets1, detc, dets2 ])
YR = np.zeros([len(detuning), hspace.dimensions], np.complex128)
widgets = [progbar.Percentage(), " ", progbar.Bar(), " ", progbar.ETA()]
pbar = progbar.ProgressBar(widgets=widgets).start()
for i in range(len(detuning)):
params.detuning = detuning[i]
pulseseq = sim.PulseSequence([sim.Rcar(params, pi / params.eta[0], 0)])
data = qc.simulateevolution(pulseseq, params, dec)
YR[i, :] = data.YR[-1, :]
pbar.update(int(1.0 * (i + 1) * 100 / (len(detuning))))
data1 = sim.database(detuning, YR, hspace, pulseseq=None, statesvalid=False)
# data1.displaypopulation(1)
data1.tracedpopulation(0)
pl.plot(detuning / 2 / pi, 1 - data1.YtrI)
pl.xlabel("relative frequency (2$\pi$ MHz)")
pl.ylabel("D state population")
pl.show()
params.stepsize = 600
detuning = np.arange(-1.2*params.omz, 1.2*params.omz, params.omz/300)
#dets1 = np.arange(-1.1*params.omz, -0.9*params.omz, 2*pi*0.001)
#detc = np.arange(-2*pi*0.1, 2*pi*0.1, 2*pi*0.001)
#dets2 = np.arange(0.9*params.omz, 1.1*params.omz, 2*pi*0.001)
#detuning = np.hstack([ dets1, detc, dets2 ])
YR = np.zeros([len(detuning),hspace.dimensions], np.complex128)
widgets = [progbar.Percentage(), ' ', progbar.Bar(),' ', progbar.ETA()]
pbar = progbar.ProgressBar(widgets=widgets).start()
for i in range(len(detuning)):
params.detuning = detuning[i]
pulseseq = sim.PulseSequence( [ \
sim.Rcar(params, pi/params.eta[0], 0), \
] )
data = qc.simulateevolution(pulseseq, params, dec)
YR[i,:] = data.YR[-1,:]
pbar.update(int(1.*(i+1)*100/(len(detuning))))
data1 = sim.database(detuning, YR, hspace, pulseseq=None, statesvalid = False)
#data1.displaypopulation(1)
data1.tracedpopulation(0)
pl.plot(detuning/2/pi, 1-data1.YtrI)
pl.xlabel('relative frequency (2$\pi$ MHz)')
pl.ylabel('D state population')
pl.show()