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,
