def reset_noise_model():
"""Return test reset noise model"""
noise_model = NoiseModel()
error1 = thermal_relaxation_error(50, 50, 0.1)
noise_model.add_all_qubit_quantum_error(error1, ['u1', 'u2', 'u3'])
error2 = error1.tensor(error1)
noise_model.add_all_qubit_quantum_error(error2, ['cx'])
return NoiseWithDescription(noise_model, "Reset Noise")
def reset_noise_model():
"""Return test reset noise model"""
noise_model = NoiseModel()
error1 = thermal_relaxation_error(50, 50, 0.1)
noise_model.add_all_qubit_quantum_error(error1, ['u1', 'u2', 'u3'])
error2 = error1.kron(error1)
noise_model.add_all_qubit_quantum_error(error2, ['cx'])
return noise_model
def thermal_noise(n_qubits, mu1, mu2, sig):
num = n_qubits
# T1 and T2 values for qubits 0-3
T1s = np.random.normal(
mu1, sig, num) # Sampled from normal distribution mean 50 microsec
T2s = np.random.normal(
mu2, sig, num) # Sampled from normal distribution mean 50 microsec
# Truncate random T2s <= T1s
T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(num)])
# Instruction times (in nanoseconds)
time_u1 = 0 # virtual gate
time_u2 = 50 # (single X90 pulse)
time_u3 = 100 # (two X90 pulses)
time_cx = 300
time_reset = 1000 # 1 microsecond
time_measure = 1000 # 1 microsecond
# QuantumError objects
errors_reset = [
thermal_relaxation_error(t1, t2, time_reset)
for t1, t2 in zip(T1s, T2s)
]
errors_measure = [
thermal_relaxation_error(t1, t2, time_measure)
for t1, t2 in zip(T1s, T2s)
]
errors_u1 = [
thermal_relaxation_error(t1, t2, time_u1) for t1, t2 in zip(T1s, T2s)
]
errors_u2 = [
thermal_relaxation_error(t1, t2, time_u2) for t1, t2 in zip(T1s, T2s)
]
errors_u3 = [
thermal_relaxation_error(t1, t2, time_u3) for t1, t2 in zip(T1s, T2s)
]
errors_cx = [[
thermal_relaxation_error(t1a, t2a, time_cx).expand(
thermal_relaxation_error(t1b, t2b, time_cx))
for t1a, t2a in zip(T1s, T2s)
] for t1b, t2b in zip(T1s, T2s)]
# Add errors to noise model
noise_thermal = NoiseModel()
for j in range(num):
noise_thermal.add_quantum_error(errors_reset[j], "reset", [j])
noise_thermal.add_quantum_error(errors_measure[j], "measure", [j])
noise_thermal.add_quantum_error(errors_u1[j], "u1", [j])
noise_thermal.add_quantum_error(errors_u2[j], "u2", [j])
noise_thermal.add_quantum_error(errors_u3[j], "u3", [j])
for k in range(num):
noise_thermal.add_quantum_error(errors_cx[j][k], "cx", [j, k])
return noise_thermal
def thermalRelaxationChannel(backend, T1s, T2s, graph, gates):
'''Method that returns the thermal relaxation error quantum channel.'''
graph = [[0,1], [1,2], [2,3]] # The two-qubit gates that we are interested in
names = [0, 1, 2, 3] # The single-qubit gates that we are interested in
# Instruction times (in nanoseconds)
time_u1 = 10 # virtual gate
time_u2 = getSQGateExecutionTime('x', backend, gates) # Average of all u2 times
time_u3 = getSQGateExecutionTime('x', backend, gates) # Average of all u3 times
time_cx = getTQGateExecutionTime('cx', backend, graph) # Average of all cx times
time_reset = 1000 # 1 microsecond
time_measure = 1000 # 1 microsecond
# QuantumError objects
errors_reset = [thermal_relaxation_error(t1, t2, time_reset)
for t1, t2 in zip(T1s, T2s)]
errors_measure = [thermal_relaxation_error(t1, t2, time_measure)
for t1, t2 in zip(T1s, T2s)]
errors_u1 = [thermal_relaxation_error(t1, t2, time_u1)
for t1, t2 in zip(T1s, T2s)]
errors_u2 = [thermal_relaxation_error(t1, t2, time_u2)
for t1, t2 in zip(T1s, T2s)]
errors_u3 = [thermal_relaxation_error(t1, t2, time_u3)
for t1, t2 in zip(T1s, T2s)]
errors_cx = [[thermal_relaxation_error(t1a, t2a, time_cx).expand(
thermal_relaxation_error(t1b, t2b, time_cx))
for t1a, t2a in zip(T1s, T2s)]
for t1b, t2b in zip(T1s, T2s)]
# Add errors to noise model
noise_thermal = NoiseModel()
for j in range(len(T1s)):
noise_thermal.add_quantum_error(errors_reset[j], "reset", [j])
noise_thermal.add_quantum_error(errors_measure[j], "measure", [j])
noise_thermal.add_quantum_error(errors_u1[j], "u1", [j])
noise_thermal.add_quantum_error(errors_u2[j], "u2", [j])
noise_thermal.add_quantum_error(errors_u3[j], "u3", [j])
for k in range(len(T1s)):
noise_thermal.add_quantum_error(errors_cx[j][k], "cx", [j, k])
return noise_thermal
def thermal_noise(mu1, mu2, sigma, p, t):
num = 2 + (2 * t - 1) * p
# T1 and T2 values for qubits 0-num
T1s = np.random.normal(
mu1, sigma, num
) # Sampled from normal distribution mean mu1 microsec, sigma = 10e3
T2s = np.random.normal(
mu2, sigma, num) # Sampled from normal distribution mean mu2 microsec
# Truncate random T2s <= T1s
T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(num)])
# Instruction times (in nanoseconds)
time_u1 = 0 # virtual gate
time_u2 = 50 # (single X90 pulse)
time_u3 = 100 # (two X90 pulses)
time_cx = 300
#Time to implement controlled/controlled-controlled evolution unitary
#Done this way to save on computational cost of simulating noise.
time_cu = 2 * (2 * time_u1 + 2 * time_cx + 2 * time_u3
) #time is 2* cu3(1).
time_ccu = 3 * (
2 * time_u1 + 2 * time_cx + 2 * time_u3
) + 2 * time_cx #ccu(2) can be decomposed into 2 ccu(1) which can be decomposed to 3 cu3(1) and 2 cx gates
# QuantumError objects
errors_u1 = [
thermal_relaxation_error(t1, t2, time_u1) for t1, t2 in zip(T1s, T2s)
]
errors_u2 = [
thermal_relaxation_error(t1, t2, time_u2) for t1, t2 in zip(T1s, T2s)
]
errors_u3 = [
thermal_relaxation_error(t1, t2, time_u3) for t1, t2 in zip(T1s, T2s)
]
errors_cx = [[
thermal_relaxation_error(t1a, t2a, time_cx).expand(
thermal_relaxation_error(t1b, t2b, time_cx))
for t1a, t2a in zip(T1s, T2s)
] for t1b, t2b in zip(T1s, T2s)]
errors_cu = [[[
thermal_relaxation_error(t1a, t2a, time_cu).expand(
thermal_relaxation_error(t1b, t2b, time_cu).expand(
thermal_relaxation_error(t1c, t2c, time_cu)))
for t1a, t2a in zip(T1s, T2s)
] for t1b, t2b in zip(T1s, T2s)] for t1c, t2c in zip(T1s, T2s)]
if num > 5: #i.e. more than 1 iteration of PEA
errors_ccu = [[[[
thermal_relaxation_error(t1a, t2a, time_ccu).expand(
thermal_relaxation_error(t1b, t2b, time_ccu).expand(
thermal_relaxation_error(t1c, t2c, time_ccu).expand(
thermal_relaxation_error(t1d, t2d, time_ccu))))
for t1a, t2a in zip(T1s, T2s)
] for t1b, t2b in zip(T1s, T2s)] for t1c, t2c in zip(T1s, T2s)]
for t1d, t2d in zip(T1s, T2s)]
# Add errors to noise model
noise_thermal = NoiseModel()
for j in range(num):
noise_thermal.add_quantum_error(errors_u1[j], "u1", [j])
noise_thermal.add_quantum_error(errors_u2[j], "u2", [j])
noise_thermal.add_quantum_error(errors_u3[j], "u3", [j])
for k in range(num):
noise_thermal.add_quantum_error(errors_cx[j][k], "cx", [j, k])
#Only Need CU and CCU gate noise between qubits that actually implement this gate!
state_qubit_0 = num - 2
state_qubit_1 = num - 1
for q in range(
t
): #CU only acts on state qubits and register from first round (in this order)
noise_thermal.add_quantum_error(
errors_cu[state_qubit_1][state_qubit_0][q], "cu",
[state_qubit_1, state_qubit_0, q])
if num > 5:
for j in range(
1, p
): #CCU acts on state qubits, or qubit from previous round and fresh register qubits (in this order)
for q in range(t * j, t * (j + 1)):
noise_thermal.add_quantum_error(
errors_ccu[state_qubit_1][state_qubit_0][p * t +
(j - 1)][q],
"ccu", [state_qubit_1, state_qubit_0, p * t + (j - 1), q])
noise_thermal.add_basis_gates(['unitary'])
return noise_thermal
4) # Sampled from normal distribution mean 50 microsec
# Truncate random T2s <= T1s
T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(4)])
# Instruction times (in nanoseconds)
time_u1 = 0 # virtual gate
time_u2 = 50 # (single X90 pulse)
time_u3 = 100 # (two X90 pulses)
time_cx = 300
time_reset = 1000 # 1 microsecond
time_measure = 1000 # 1 microsecond
# QuantumError objects
errors_reset = [
thermal_relaxation_error(t1, t2, time_reset) for t1, t2 in zip(T1s, T2s)
]
errors_measure = [
thermal_relaxation_error(t1, t2, time_measure) for t1, t2 in zip(T1s, T2s)
]
errors_u1 = [
thermal_relaxation_error(t1, t2, time_u1) for t1, t2 in zip(T1s, T2s)
]
errors_u2 = [
thermal_relaxation_error(t1, t2, time_u2) for t1, t2 in zip(T1s, T2s)
]
errors_u3 = [
thermal_relaxation_error(t1, t2, time_u3) for t1, t2 in zip(T1s, T2s)
]
errors_cx = [[
thermal_relaxation_error(t1a, t2a, time_cx).expand(
