def testInversion(self):
'''
Try a simple three level SC qubit system and see if can prepare the excited state.
'''
#Setup a three level qubit and a 100MHz delta
Q1 = SCQubit(3, 4.987456e9, -100e6, name='Q1')
systemParams = SystemParams()
systemParams.add_sub_system(Q1)
systemParams.add_control_ham(
inphase=Hamiltonian(0.5 * (Q1.loweringOp + Q1.raisingOp)),
quadrature=Hamiltonian(0.5 *
(-1j * Q1.loweringOp + 1j * Q1.raisingOp)))
systemParams.create_full_Ham()
systemParams.measurement = Q1.levelProjector(1)
#Setup the pulse parameters for the optimization
pulseParams = PulseParams()
pulseParams.timeSteps = 1e-9 * np.ones(30)
pulseParams.rhoStart = Q1.levelProjector(0)
pulseParams.rhoGoal = Q1.levelProjector(1)
pulseParams.add_control_line(freq=-Q1.omega)
pulseParams.H_int = Hamiltonian(Q1.omega * np.diag(np.arange(Q1.dim)))
pulseParams.optimType = 'state2state'
#Call the optimization
optimize_pulse(pulseParams, systemParams)
#Now test the optimized pulse and make sure it puts all the population in the excited state
result = simulate_sequence(pulseParams,
systemParams,
pulseParams.rhoStart,
simType='unitary')[0]
assert result > 0.99
def testInversion(self):
'''
Try a simple three level SC qubit system and see if can prepare the excited state.
'''
#Setup a three level qubit and a 100MHz delta
Q1 = SCQubit(3, 4.987456e9, -100e6, name='Q1')
systemParams = SystemParams()
systemParams.add_sub_system(Q1)
systemParams.add_control_ham(inphase = Hamiltonian(0.5*(Q1.loweringOp + Q1.raisingOp)), quadrature = Hamiltonian(0.5*(-1j*Q1.loweringOp + 1j*Q1.raisingOp)))
systemParams.create_full_Ham()
systemParams.measurement = Q1.levelProjector(1)
#Setup the pulse parameters for the optimization
pulseParams = PulseParams()
pulseParams.timeSteps = 1e-9*np.ones(30)
pulseParams.rhoStart = Q1.levelProjector(0)
pulseParams.rhoGoal = Q1.levelProjector(1)
pulseParams.add_control_line(freq=-Q1.omega)
pulseParams.H_int = Hamiltonian(Q1.omega*np.diag(np.arange(Q1.dim)))
pulseParams.optimType = 'state2state'
#Call the optimization
optimize_pulse(pulseParams, systemParams)
#Now test the optimized pulse and make sure it puts all the population in the excited state
result = simulate_sequence(pulseParams, systemParams, pulseParams.rhoStart, simType='unitary')[0]
assert result > 0.99
def testDRAG(self):
'''
Try a unitary inversion pulse on a three level SCQuibt and see if we get something close to DRAG
'''
#Setup a three level qubit and a 100MHz delta
Q1 = SCQubit(3, 4.987456e9, -150e6, name='Q1')
systemParams = SystemParams()
systemParams.add_sub_system(Q1)
systemParams.add_control_ham(inphase = Hamiltonian(0.5*(Q1.loweringOp + Q1.raisingOp)), quadrature = Hamiltonian(0.5*(-1j*Q1.loweringOp + 1j*Q1.raisingOp)))
systemParams.add_control_ham(inphase = Hamiltonian(0.5*(Q1.loweringOp + Q1.raisingOp)), quadrature = Hamiltonian(0.5*(-1j*Q1.loweringOp + 1j*Q1.raisingOp)))
systemParams.create_full_Ham()
systemParams.measurement = Q1.levelProjector(1)
#Setup the pulse parameters for the optimization
numPoints = 30
pulseTime = 15e-9
pulseParams = PulseParams()
pulseParams.timeSteps = (pulseTime/numPoints)*np.ones(numPoints)
pulseParams.rhoStart = Q1.levelProjector(0)
pulseParams.rhoGoal = Q1.levelProjector(1)
pulseParams.Ugoal = Q1.pauliX
pulseParams.add_control_line(freq=-Q1.omega, bandwidth=300e6, maxAmp=200e6)
pulseParams.add_control_line(freq=-Q1.omega, phase=-np.pi/2, bandwidth=300e6, maxAmp=200e6)
pulseParams.H_int = Hamiltonian((Q1.omega)*np.diag(np.arange(Q1.dim)))
pulseParams.optimType = 'unitary'
pulseParams.derivType = 'finiteDiff'
#Start with a Gaussian
tmpGauss = np.exp(-np.linspace(-2,2,numPoints)**2)
tmpScale = 0.5/(np.sum(pulseParams.timeSteps*tmpGauss))
pulseParams.startControlAmps = np.vstack((tmpScale*tmpGauss, np.zeros(numPoints)))
#Call the optimization
optimize_pulse(pulseParams, systemParams)
if plotResults:
plt.plot(np.cumsum(pulseParams.timeSteps)*1e9,pulseParams.controlAmps.T/1e6);
plt.ylabel('Pulse Amplitude (MHz)')
plt.xlabel('Time (ns)')
plt.legend(('X Quadrature', 'Y Quadrature'))
plt.title('DRAG Pulse from Optimal Control')
plt.show()
#Now test the optimized pulse and make sure it does give us the desired unitary
result = simulate_sequence(pulseParams, systemParams, pulseParams.rhoStart, simType='unitary')
assert np.abs(np.trace(np.dot(result[1].conj().T, pulseParams.Ugoal)))**2/np.abs(np.trace(np.dot(pulseParams.Ugoal.conj().T, pulseParams.Ugoal)))**2 > 0.99
def testDRAG(self):
'''
Try a unitary inversion pulse on a three level SCQuibt and see if we get something close to DRAG
'''
#Setup a three level qubit and a 100MHz delta
Q1 = SCQubit(3, 4.987456e9, -150e6, name='Q1')
systemParams = SystemParams()
systemParams.add_sub_system(Q1)
systemParams.add_control_ham(
inphase=Hamiltonian(0.5 * (Q1.loweringOp + Q1.raisingOp)),
quadrature=Hamiltonian(0.5 *
(-1j * Q1.loweringOp + 1j * Q1.raisingOp)))
systemParams.add_control_ham(
inphase=Hamiltonian(0.5 * (Q1.loweringOp + Q1.raisingOp)),
quadrature=Hamiltonian(0.5 *
(-1j * Q1.loweringOp + 1j * Q1.raisingOp)))
systemParams.create_full_Ham()
systemParams.measurement = Q1.levelProjector(1)
#Setup the pulse parameters for the optimization
numPoints = 30
pulseTime = 15e-9
pulseParams = PulseParams()
pulseParams.timeSteps = (pulseTime / numPoints) * np.ones(numPoints)
pulseParams.rhoStart = Q1.levelProjector(0)
pulseParams.rhoGoal = Q1.levelProjector(1)
pulseParams.Ugoal = Q1.pauliX
pulseParams.add_control_line(freq=-Q1.omega,
bandwidth=300e6,
maxAmp=200e6)
pulseParams.add_control_line(freq=-Q1.omega,
phase=-np.pi / 2,
bandwidth=300e6,
maxAmp=200e6)
pulseParams.H_int = Hamiltonian(
(Q1.omega) * np.diag(np.arange(Q1.dim)))
pulseParams.optimType = 'unitary'
pulseParams.derivType = 'finiteDiff'
#Start with a Gaussian
tmpGauss = np.exp(-np.linspace(-2, 2, numPoints)**2)
tmpScale = 0.5 / (np.sum(pulseParams.timeSteps * tmpGauss))
pulseParams.startControlAmps = np.vstack(
(tmpScale * tmpGauss, np.zeros(numPoints)))
#Call the optimization
optimize_pulse(pulseParams, systemParams)
if plotResults:
plt.plot(
np.cumsum(pulseParams.timeSteps) * 1e9,
pulseParams.controlAmps.T / 1e6)
plt.ylabel('Pulse Amplitude (MHz)')
plt.xlabel('Time (ns)')
plt.legend(('X Quadrature', 'Y Quadrature'))
plt.title('DRAG Pulse from Optimal Control')
plt.show()
#Now test the optimized pulse and make sure it does give us the desired unitary
result = simulate_sequence(pulseParams,
systemParams,
pulseParams.rhoStart,
simType='unitary')
assert np.abs(np.trace(np.dot(
result[1].conj().T, pulseParams.Ugoal)))**2 / np.abs(
np.trace(np.dot(pulseParams.Ugoal.conj().T,
pulseParams.Ugoal)))**2 > 0.99
DRAGResults = simulate_sequence_stack(pulseSeqs,
systemParams,
rhoIn,
simType='lindblad')[0]
plt.plot(pulseTimes * 1e9, DRAGResults)
'''State to State Optimal Control'''
numSteps = 100
pulseTimes = 1e-9 * np.arange(3, 25, 3)
results = np.zeros_like(pulseTimes)
for ct, pulseTime in enumerate(pulseTimes):
pulseParams = PulseParams()
pulseParams.timeSteps = (pulseTime / numSteps) * np.ones(numSteps)
pulseParams.rhoStart = qubit.levelProjector(0)
pulseParams.rhoGoal = qubit.levelProjector(1)
pulseParams.add_control_line(freq=0, phase=0)
pulseParams.add_control_line(freq=0, phase=-pi / 2)
pulseParams.type = 'state2state'
#Call the optimization
optimize_pulse(pulseParams, systemParams)
#Now test the optimized pulse and make sure it puts all the population in the excited state
results[ct] = simulate_sequence(pulseParams,
systemParams,
pulseParams.rhoStart,
simType='lindblad')[0]
plt.plot(pulseTimes * 1e9, results, '*')
plt.xlabel('Pulse Time (ns)')
plt.ylabel('Population in First Excited State')
#systemParams.dissipators.append(Dissipator(systemParams.expand_operator('Q2', Q2.T1Dissipator)))
#
##Setup the initial state as the ground state
#rhoIn = np.zeros((systemParams.dim, systemParams.dim))
#rhoIn[0,0] = 1
sampRate = 1.2e9
timeStep = 1.0 / sampRate
drive1Freq = Q1.omega - 1e6
drive2Freq = Q2.omega - 1e6
pulseParams = PulseParams()
pulseParams.timeSteps = timeStep * np.ones(144)
pulseParams.add_control_line(freq=-drive1Freq,
phase=0,
bandwidth=300e6,
maxAmp=100e6)
pulseParams.add_control_line(freq=-drive1Freq,
phase=-pi / 2,
bandwidth=300e6,
maxAmp=100e6)
#pulseParams.add_control_line(freq=-drive1Freq, phase=0)
#pulseParams.add_control_line(freq=-drive1Freq, phase=pi/2)
#pulseParams.add_control_line(freq=-drive2Freq, phase=0, bandwidth=300e6, maxAmp=100e6)
#pulseParams.add_control_line(freq=-drive2Freq, phase=-pi/2, bandwidth=300e6, maxAmp=100e6)
#pulseParams.H_int = Hamiltonian(systemParams.expand_operator('Q1', drive1Freq*Q1.numberOp) + systemParams.expand_operator('Q2', drive2Freq*Q2.numberOp))
pulseParams.optimType = 'unitary'
Q2Goal = np.eye(3, dtype=np.complex128)
Q2Goal[2, 2] = 0
pulseParams.Ugoal = np.kron(Q1.pauliX, Q2Goal)
DRAGResults = simulate_sequence_stack(pulseSeqs, systemParams, rhoIn, simType='lindblad')[0]
plt.plot(pulseTimes*1e9,DRAGResults)
'''State to State Optimal Control'''
numSteps = 100
pulseTimes = 1e-9*np.arange(3,25,3)
results = np.zeros_like(pulseTimes)
for ct, pulseTime in enumerate(pulseTimes):
pulseParams = PulseParams()
pulseParams.timeSteps = (pulseTime/numSteps)*np.ones(numSteps)
pulseParams.rhoStart = qubit.levelProjector(0)
pulseParams.rhoGoal = qubit.levelProjector(1)
pulseParams.add_control_line(freq=0, phase=0)
pulseParams.add_control_line(freq=0, phase=-pi/2)
pulseParams.type = 'state2state'
#Call the optimization
optimize_pulse(pulseParams, systemParams)
#Now test the optimized pulse and make sure it puts all the population in the excited state
results[ct] = simulate_sequence(pulseParams, systemParams, pulseParams.rhoStart, simType='lindblad')[0]
plt.plot(pulseTimes*1e9,results,'*')
plt.xlabel('Pulse Time (ns)')
plt.ylabel('Population in First Excited State')
plt.title('Excited State Preparation with T1 = {0:.0f}ns'.format(1e9*qubit.T1))
plt.legend(('Square','Gaussian','DRAG','Optimal Control'))
plt.show()