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_qiskit_simulator'
qp = QuantumProgram()
if shots != 1:
H = group_paulis(pauli_list)
entangler_map = qp.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
