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 test_prepstate(self):
n_qubit = 4
dim = 2**n_qubit
from qulacs import QuantumState
from qulacs.state import inner_product
s0 = QuantumState(n_qubit)
s0.set_Haar_random_state()
s1 = freqerica.circuit.universal.prepstate(n_qubit, s0.get_vector())
print(inner_product(s0, s1))
civec = {0b0011: .5, 0b0101: +.5j, 0b1001: -.5j, 0b0110: -.5}
s2 = freqerica.circuit.universal.prepstate(n_qubit, civec)
print(s2)
assert True
# cost function after training
#print(result.fun)
# theta after training
theta_opt = result.x
print(theta_opt)
# inference over test data only
n_data = 20
X = list()
# Put the optimized theta to U_out
set_U_out(theta_opt)
input_value = list()
for i in range(n_data):
state.set_Haar_random_state()
#x = random.random()
#input_value.append(x)
state.set_zero_state() # U_in|000>
# Input test data only
U_in(input_test[i]).update_quantum_state(state)
.append(qcl_pred(state, U_out))
plt.plot(input_value, X, 'o', color='black')
plt.show()
plt.savefig('qcl_regression.png')
