def expval(self, observable):
if self.analytic:
qulacs_observable = Observable(self.num_wires)
if isinstance(observable.name, list):
observables = [
self._observable_map[obs] for obs in observable.name
]
else:
observables = [self._observable_map[observable.name]]
if None not in observables:
applied_wires = self.map_wires(observable.wires).tolist()
opp = " ".join([
f"{obs} {applied_wires[i]}"
for i, obs in enumerate(observables)
])
qulacs_observable.add_operator(1.0, opp)
return qulacs_observable.get_expectation_value(
self._pre_rotated_state)
# exact expectation value
eigvals = self._asarray(observable.eigvals, dtype=self.R_DTYPE)
prob = self.probability(wires=observable.wires)
return self._dot(eigvals, prob)
# estimate the ev
return np.mean(self.sample(observable))
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 qtest(angle):
state = QuantumState(3)
state.set_Haar_random_state()
circuit = QuantumCircuit(3)
circuit.add_X_gate(0)
merged_gate = merge(CNOT(0, 1), Y(1))
circuit.add_gate(merged_gate)
circuit.add_RX_gate(1, angle)
circuit.update_quantum_state(state)
observable = Observable(3)
observable.add_operator(2.0, "X 2 Y 1 Z 0")
observable.add_operator(-3.0, "Z 2")
result = observable.get_expectation_value(state)
output = {'energy': result}
return (output)
def convert_openfermion_op(n_qubit, openfermion_op):
"""convert_openfermion_op
Args:
n_qubit (:class:`int`)
openfermion_op (:class:`openfermion.ops.QubitOperator`)
Returns:
:class:`qulacs.Observable`
"""
ret = Observable(n_qubit)
for pauli_product in openfermion_op.terms:
coef = float(np.real(openfermion_op.terms[pauli_product]))
pauli_string = ''
for pauli_operator in pauli_product:
pauli_string += pauli_operator[1] + ' ' + str(pauli_operator[0])
pauli_string += ' '
ret.add_operator(coef, pauli_string[:-1])
return ret
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()
def test_observable(self):
from qulacs import Observable
obs = Observable(1)
obs.add_operator(1.0, "X 0")
term = obs.get_term(0)
del term
U.add_RZ_gate(i, angle_z)
return U
# Function to update theta
def set_U_out(theta):
global U_out
parameter_count = U_out.get_parameter_count()
for i in range(parameter_count):
U_out.set_parameter(i, theta[i])
# Construct an observable gate
obs = Observable(nqubit)
obs.add_operator(1, "Z 0")
obs.add_operator(2, "Z 1")
#obs.add_operator(4, "Z 2")
# Function for prediction
def qcl_pred(x, U_out):
# Output state
U_out.update_quantum_state(x)
# Output from the model
res = obs.get_expectation_value(state)
return res
# cost function L
#circuit.add_H_gate(2)
circuit.add_RX_gate(0, 0.1)
circuit.add_RX_gate(1, 0.1)
#circuit.add_RX_gate(2, 0.1)
merged_gate = merge(CNOT(0,1),Y(1))
circuit.add_gate(merged_gate)
meas0 = Measurement(0, 0)
circuit.add_gate(meas0)
meas1 = Measurement(1, 1)
circuit.add_gate(meas1)
#meas2 = Measurement(2, 2)
#circuit.add_gate(meas2)
print(circuit)
observable = Observable(nqubit)
observable.add_operator(1, "Z 0")
observable.add_operator(2, "Z 1")
#observable.add_operator(4, "Z 2")
data = list()
x_value = list()
y_value = list()
for i in range(n_data):
#state.set_Haar_random_state()
x = random.random()
state.set_zero_state() # U_in|000>
U_in(x).update_quantum_state(state)
state_vec = state.get_vector()
circuit.update_quantum_state(state)
value = observable.get_expectation_value(state)
parameter_count = U_out.get_parameter_count()
theta_init = [U_out.get_parameter(ind) for ind in range(parameter_count)]
# Function to update theta
def set_U_out(theta):
global U_out
parameter_count = U_out.get_parameter_count()
for i in range(parameter_count):
U_out.set_parameter(i, theta[i])
# Construct an observable gate
from qulacs import Observable
obs = Observable(nqubit)
obs.add_operator(2.,'Z 0') # Add an operator 2*Z. This number should be optimised.
# Function for prediction
def qcl_pred(x, U_out):
state = QuantumState(nqubit)
state.set_zero_state()
# Input state
U_in(x).update_quantum_state(state)
# Output state
U_out.update_quantum_state(state)
# Output from the model
res = obs.get_expectation_value(state)
