def main():
import numpy as np
n_qubit = 2
obs = Observable(n_qubit)
initial_state = QuantumState(n_qubit)
obs.add_operator(1, "Z 0 Z 1")
circuit_list = []
p_list = [0.02, 0.04, 0.06, 0.08]
#prepare circuit list
for p in p_list:
circuit = QuantumCircuit(n_qubit)
circuit.add_H_gate(0)
circuit.add_RY_gate(1, np.pi / 6)
circuit.add_CNOT_gate(0, 1)
circuit.add_gate(
Probabilistic([p / 4, p / 4, p / 4],
[X(0), Y(0), Z(0)])) #depolarizing noise
circuit.add_gate(
Probabilistic([p / 4, p / 4, p / 4],
[X(1), Y(1), Z(1)])) #depolarizing noise
circuit_list.append(circuit)
#get mitigated output
mitigated, non_mitigated_array, fit_coefs = error_mitigation_extrapolate_linear(
circuit_list,
p_list,
initial_state,
obs,
n_circuit_sample=100000,
return_full=True)
#plot the result
p = np.linspace(0, max(p_list), 100)
plt.plot(p,
fit_coefs[0] * p + fit_coefs[1],
linestyle="--",
label="linear fit")
plt.scatter(p_list, non_mitigated_array, label="un-mitigated")
plt.scatter(0, mitigated, label="mitigated output")
#prepare the clean result
state = QuantumState(n_qubit)
circuit = QuantumCircuit(n_qubit)
circuit.add_H_gate(0)
circuit.add_RY_gate(1, np.pi / 6)
circuit.add_CNOT_gate(0, 1)
circuit.update_quantum_state(state)
plt.scatter(0, obs.get_expectation_value(state), label="True output")
plt.xlabel("error rate")
plt.ylabel("expectation value")
plt.legend()
plt.show()
def test_add_same_gate_multiple_time(self):
from qulacs import QuantumCircuit, QuantumState
from qulacs.gate import X, DepolarizingNoise, DephasingNoise, Probabilistic, RX
state = QuantumState(1)
circuit = QuantumCircuit(1)
noise = DepolarizingNoise(0, 0)
circuit.add_gate(noise)
circuit.add_gate(noise.copy())
circuit.add_gate(DephasingNoise(0, 0))
circuit.add_gate(Probabilistic([0.1], [RX(0, 0)]))
gate = RX(0, 0)
circuit.add_gate(gate)
circuit.add_gate(gate)
circuit.add_gate(gate)
circuit.add_gate(gate)
circuit.add_gate(gate)
del gate
circuit.update_quantum_state(state)
circuit.update_quantum_state(state)
circuit.update_quantum_state(state)
circuit.update_quantum_state(state)
circuit.update_quantum_state(state)
circuit.update_quantum_state(state)
circuit.update_quantum_state(state)
circuit.to_string()
del circuit
del state
def test_circuit_add_gate(self):
from qulacs import QuantumCircuit, QuantumState
from qulacs.gate import Identity, X, Y, Z, H, S, Sdag, T, Tdag, sqrtX, sqrtXdag, sqrtY, sqrtYdag
from qulacs.gate import P0, P1, U1, U2, U3, RX, RY, RZ, CNOT, CZ, SWAP, TOFFOLI, FREDKIN, Pauli, PauliRotation
from qulacs.gate import DenseMatrix, SparseMatrix, DiagonalMatrix, RandomUnitary, ReversibleBoolean, StateReflection
from qulacs.gate import BitFlipNoise, DephasingNoise, IndependentXZNoise, DepolarizingNoise, TwoQubitDepolarizingNoise, AmplitudeDampingNoise, Measurement
from qulacs.gate import merge, add, to_matrix_gate, Probabilistic, CPTP, Instrument, Adaptive
from scipy.sparse import lil_matrix
qc = QuantumCircuit(3)
qs = QuantumState(3)
ref = QuantumState(3)
sparse_mat = lil_matrix((4, 4))
sparse_mat[0, 0] = 1
sparse_mat[1, 1] = 1
def func(v, d):
return (v + 1) % d
def adap(v):
return True
gates = [
Identity(0), X(0), Y(0), Z(0), H(0), S(0), Sdag(0), T(0), Tdag(0), sqrtX(0), sqrtXdag(0), sqrtY(0), sqrtYdag(0),
Probabilistic([0.5, 0.5], [X(0), Y(0)]), CPTP([P0(0), P1(0)]), Instrument([P0(0), P1(0)], 1), Adaptive(X(0), adap),
CNOT(0, 1), CZ(0, 1), SWAP(0, 1), TOFFOLI(0, 1, 2), FREDKIN(0, 1, 2), Pauli([0, 1], [1, 2]), PauliRotation([0, 1], [1, 2], 0.1),
DenseMatrix(0, np.eye(2)), DenseMatrix([0, 1], np.eye(4)), SparseMatrix([0, 1], sparse_mat),
DiagonalMatrix([0, 1], np.ones(4)), RandomUnitary([0, 1]), ReversibleBoolean([0, 1], func), StateReflection(ref),
BitFlipNoise(0, 0.1), DephasingNoise(0, 0.1), IndependentXZNoise(0, 0.1), DepolarizingNoise(0, 0.1), TwoQubitDepolarizingNoise(0, 1, 0.1),
AmplitudeDampingNoise(0, 0.1), Measurement(0, 1), merge(X(0), Y(1)), add(X(0), Y(1)), to_matrix_gate(X(0)),
P0(0), P1(0), U1(0, 0.), U2(0, 0., 0.), U3(0, 0., 0., 0.), RX(0, 0.), RY(0, 0.), RZ(0, 0.),
]
gates.append(merge(gates[0], gates[1]))
gates.append(add(gates[0], gates[1]))
ref = None
for gate in gates:
qc.add_gate(gate)
for gate in gates:
qc.add_gate(gate)
qc.update_quantum_state(qs)
qc = None
qs = None
for gate in gates:
gate = None
gates = None
parametric_gates = None
def test_VqeOptimizer():
from qulacs import ParametricQuantumCircuit
from qulacs import QuantumState
from qulacs import Observable
from qulacs.gate import Probabilistic, X, Y, Z
import numpy as np
import matplotlib.pyplot as plt
n_qubit = 2
p_list = [0.05, 0.1, 0.15]
parametric_circuit_list = \
[ParametricQuantumCircuit(n_qubit)
for i in range(len(p_list))]
initial_state = QuantumState(n_qubit)
for (p, circuit) in zip(p_list, parametric_circuit_list):
circuit.add_H_gate(0)
circuit.add_parametric_RY_gate(1, np.pi / 6)
circuit.add_CNOT_gate(0, 1)
prob = Probabilistic([p / 4, p / 4, p / 4], [X(0), Y(0), Z(0)])
circuit.add_gate(prob)
noiseless_circuit = ParametricQuantumCircuit(n_qubit)
noiseless_circuit.add_H_gate(0)
noiseless_circuit.add_parametric_RY_gate(1, np.pi / 6)
noiseless_circuit.add_CNOT_gate(0, 1)
n_sample_per_circuit = 1
n_circuit_sample = 1000
obs = Observable(n_qubit)
obs.add_operator(1.0, "Z 0 Z 1")
obs.add_operator(0.5, "X 0 X 1")
initial_param = np.array([np.pi / 6])
opt = VqeOptimizer(parametric_circuit_list,
initial_state,
obs,
initial_param,
p_list,
n_circuit_sample=n_circuit_sample,
n_sample_per_circuit=n_sample_per_circuit,
noiseless_circuit=noiseless_circuit)
noisy = opt.sample_output(initial_param)
mitigated, exp_array, _ = opt.sample_mitigated_output(initial_param,
return_full=True)
exact = opt.exact_output(initial_param)
print(noisy, exact)
print(exp_array, mitigated)
opt_param = opt.optimize()
print(opt_param)
theta_list = np.linspace(0, np.pi, 100)
output_list = [opt.exact_output([theta]) for theta in theta_list]
plt.plot(theta_list,
output_list,
color="black",
linestyle="dashed",
label="exact")
plt.scatter(opt.parameter_history,
opt.exp_history,
c="blue",
label="optimization history")
plt.xlabel("theta")
plt.ylabel("output")
plt.legend()
plt.show()
from qulacs import Observable
from qulacs import QuantumState, QuantumCircuit
from qulacs.gate import Probabilistic, X
import matplotlib.pyplot as plt
obs = Observable(1)
obs.add_operator(1, "Z 0")
state = QuantumState(1)
circuit = QuantumCircuit(1)
p = 0.1 # probability of bit flip
n_circuit_sample = 10000
n_depth = 20 # the number of probabilistic gate
probabilistic_pauli_gate = Probabilistic([p],
[X(0)]) #define probabilistic gate
circuit.add_gate(probabilistic_pauli_gate) # add the prob. gate to the circuit
exp_array = []
for depth in range(n_depth):
exp = 0
for i in [0] * n_circuit_sample:
state.set_zero_state()
for _ in range(depth):
circuit.update_quantum_state(state) # apply the prob. gate
exp += obs.get_expectation_value(
state) # get expectation value for one sample of circuit
exp /= n_circuit_sample # get overall average
exp_array.append(exp)