i = 0 for R in tqdm(Rs): h, v, Enuc, E = get_H2(R) #fci_coeffs[i] = FCI(h,v) FCIs[i] = E['fci'] HFs[i] = E['hf'] h2 = SecondQuantizedHamiltonian(n, l, h, v, nuclear_repulsion=Enuc, add_spin=True) h2.group_paulis(qwc=True, gc=True) model = VQE(h2, Minimizer('Cobyla', tol=1 / (10 * shots), disp=False), 'RYRZ', options=options) theta = model.optimize() Es[i], VARs[i] = model.get_mean(theta, N=10000, M=10) #vqe_coeffs[i] = model.get_state_coeffs(theta) i += 1 np.save('RYRZ/bonds.npy', Rs) np.save('RYRZ/fci.npy', FCIs) np.save('RYRZ/hf.npy', HFs) np.save('RYRZ/E.npy', Es) np.save('RYRZ/var.npy', VARs)
from optimizer import Minimizer from vqe import VQE l = 4 # number of spin orbitals / number of qubits n = 2 # Number of occupied spin orbitals omega = 1 h, v, E = get_qdot_matrix(n, l, omega) print(E, FCI(n, l, h, v)) qdot = SecondQuantizedHamiltonian(n, l, h, v, anti_symmetric=True) qdot.group_paulis() options = { 'shots': 10000, 'print': True, 'device': 'ibmq_london', 'noise_model': True, 'meas_fit': True, 'coupling_map': True, 'layout': [1, 0, 2, 3], 'basis_gates': True } model = VQE(qdot, Minimizer('Cobyla'), 'RYPAIRING', ansatz_depth=1,