def test_quantum_walk():
init_machine = InitQMachine()
machine = init_machine.m_machine
x = machine.cAlloc()
data = [3, 6, 6, 9, 10, 15, 11, 6]
#data=[3]
measure_qubits = pq.QVec()
grover_cir = pq.quantum_walk_alg(data, x == 6, machine, measure_qubits, 2)
result = pq.prob_run_dict(grover_cir, measure_qubits)
print(result)
def test_grover1(self):
init_machine = InitQMachine()
machine = init_machine.m_machine
x = machine.cAlloc()
data = [8, 7, 6, 0, 6, 3, 6, 4, 21, 15, 11, 11, 3, 9, 7]
measure_qubits = pq.QVec()
grover_cir = pq.Grover(data, x == 6, machine, measure_qubits, 1)
# print(grover_cir)
# draw_qprog(grover_cir, 'pic', filename='D:/cir_grover_1.jpg', verbose=True)
result = pq.prob_run_dict(grover_cir, measure_qubits)
def cal_circuit(thetas):
pq.init(pq.QMachineType.CPU)
qubitlist = pq.qAlloc_many(1)
prog = pq.QProg()
for theta, qubit in zip(thetas, qubitlist):
prog.insert(pq.H(qubit))
prog.insert(pq.RY(qubit, theta))
result = pq.prob_run_dict(prog, qubitlist)
states = []
probabilities = []
for key, val in result.items():
states.append(int(key, 2))
probabilities.append(val)
states = np.array(states, 'float')
probabilities = np.array(probabilities)
expectation = np.sum(states * probabilities)
pq.finalize()
return np.array([expectation])
