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