本文整理汇总了Python中qiskit.QuantumProgram.get_backend_configuration方法的典型用法代码示例。如果您正苦于以下问题：Python QuantumProgram.get_backend_configuration方法的具体用法？Python QuantumProgram.get_backend_configuration怎么用？Python QuantumProgram.get_backend_configuration使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类qiskit.QuantumProgram的用法示例。
在下文中一共展示了QuantumProgram.get_backend_configuration方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_get_backend_configuration
# 需要导入模块: from qiskit import QuantumProgram [as 别名]
# 或者: from qiskit.QuantumProgram import get_backend_configuration [as 别名]
def test_get_backend_configuration(self):
"""Test get_backend_configuration.
If all correct should return configuration for the
local_qasm_simulator.
"""
qp = QuantumProgram(specs=QPS_SPECS)
test = len(qp.get_backend_configuration("local_qasm_simulator"))
self.assertEqual(test, 6)示例2: vqe
# 需要导入模块: from qiskit import QuantumProgram [as 别名]
# 或者: from qiskit.QuantumProgram import get_backend_configuration [as 别名]
def vqe(molecule='H2', depth=6, max_trials=200, shots=1):
if molecule == 'H2':
n_qubits = 2
Z1 = 1
Z2 = 1
min_distance = 0.2
max_distance = 4
elif molecule == 'LiH':
n_qubits = 4
Z1 = 1
Z2 = 3
min_distance = 0.5
max_distance = 5
else:
raise QISKitError("Unknown molecule for VQE.")
# Read Hamiltonian
ham_name = os.path.join(os.path.dirname(__file__),
molecule + '/' + molecule + 'Equilibrium.txt')
pauli_list = Hamiltonian_from_file(ham_name)
H = make_Hamiltonian(pauli_list)
# Exact Energy
exact = np.amin(la.eig(H)[0]).real
print('The exact ground state energy is: {}'.format(exact))
# Optimization
device = 'local_qasm_simulator'
qp = QuantumProgram()
if shots != 1:
H = group_paulis(pauli_list)
entangler_map = qp.get_backend_configuration(device)['coupling_map']
if entangler_map == 'all-to-all':
entangler_map = {i: [j for j in range(n_qubits) if j != i] for i in range(n_qubits)}
else:
entangler_map = mapper.coupling_list2dict(entangler_map)
initial_theta = np.random.randn(2 * n_qubits * depth) # initial angles
initial_c = 0.01 # first theta perturbations
target_update = 2 * np.pi * 0.1 # aimed update on first trial
save_step = 20 # print optimization trajectory
cost = partial(cost_function, qp, H, n_qubits, depth, entangler_map, shots, device)
SPSA_params = SPSA_calibration(cost, initial_theta, initial_c, target_update, stat=25)
output = SPSA_optimization(cost, initial_theta, SPSA_params, max_trials, save_step, last_avg=1)
return qp示例3: pprint
# 需要导入模块: from qiskit import QuantumProgram [as 别名]
# 或者: from qiskit.QuantumProgram import get_backend_configuration [as 别名]
circuits = ['Bell']
qobj = qp.compile(circuits, backend)
result = qp.run(qobj, wait=2, timeout=240)
#print(result.get_counts('Bell'))
pprint(qp.available_backends())
#pprint(qp.get_backend_status('ibmqx2'))
pprint(qp.get_backend_configuration('ibmqx5'))
# quantum register for the first circuit
q1 = qp.create_quantum_register('q1', 4)
c1 = qp.create_classical_register('c1', 4)
# quantum register for the second circuit
q2 = qp.create_quantum_register('q2', 2)
c2 = qp.create_classical_register('c2', 2)
# making the first circuits
qc1 = qp.create_circuit('GHZ', [q1], [c1])
qc2 = qp.create_circuit('superpostion', [q2], [c2])
qc1.h(q1[0])
qc1.cx(q1[0], q1[1])