def test():
r = 1.5
geometry = [('H', (0,0,1*r)), ('H', (0,0,2*r)), ('H', (0,0,3*r)), ('H', (0,0,4*r))]
charge = 0
spin = 0
basis = 'sto-3g'
[n_orb, n_a, n_b, h, g, mol, E_nuc, E_scf, C, S] = pyscf_helper.init(geometry,charge,spin,basis)
print(" n_orb: %4i" %n_orb)
print(" n_a : %4i" %n_a)
print(" n_b : %4i" %n_b)
sq_ham = pyscf_helper.SQ_Hamiltonian()
sq_ham.init(h, g, C, S)
print(" HF Energy: %12.8f" %(E_nuc + sq_ham.energy_of_determinant(range(n_a),range(n_b))))
ehf = E_nuc + sq_ham.energy_of_determinant(range(n_a),range(n_b))
assert(abs(ehf - -1.82913741) < 1e-7)
fermi_ham = sq_ham.export_FermionOperator()
hamiltonian = openfermion.transforms.get_sparse_operator(fermi_ham)
s2 = vqe_methods.Make_S2(n_orb)
#build reference configuration
occupied_list = []
for i in range(n_a):
occupied_list.append(i*2)
for i in range(n_b):
occupied_list.append(i*2+1)
print(" Build reference state with %4i alpha and %4i beta electrons" %(n_a,n_b), occupied_list)
reference_ket = scipy.sparse.csc_matrix(openfermion.jw_configuration_state(occupied_list, 2*n_orb)).transpose()
[e,v] = scipy.sparse.linalg.eigsh(hamiltonian.real,1,which='SA',v0=reference_ket.todense())
for ei in range(len(e)):
S2 = v[:,ei].conj().T.dot(s2.dot(v[:,ei]))
print(" State %4i: %12.8f au <S2>: %12.8f" %(ei,e[ei]+E_nuc,S2))
fermi_ham += FermionOperator((),E_nuc)
pyscf.molden.from_mo(mol, "full.molden", sq_ham.C)
# Francesco, change this to singlet_GSD() if you want generalized singles and doubles
pool = operator_pools.singlet_SD()
pool.init(n_orb, n_occ_a=n_a, n_occ_b=n_b, n_vir_a=n_orb-n_a, n_vir_b=n_orb-n_b)
[e,v,params] = vqe_methods.adapt_vqe(fermi_ham, pool, reference_ket, theta_thresh=1e-9)
print(" Final ADAPT-VQE energy: %12.8f" %e)
print(" <S^2> of final state : %12.8f" %(v.conj().T.dot(s2.dot(v))[0,0].real))
assert(abs(-1.99471290 - e) < 1e-7)
import vqe_methods
import operator_pools
import sys
r = 2.684
geometry = [('H', (0, 0, -r)), ('Be', (0, 0, 0)), ('H', (0, 0, r))]
filename = "beh2_r2684_adapt_sd.out"
sys.stdout = open(filename, 'w')
vqe_methods.adapt_vqe(geometry,
pool=operator_pools.singlet_SD(),
adapt_thresh=1e-7)
print(" Finished: %20.12f" % trial_model.curr_energy)
print(" -----------New ansatz----------- ")
print(" %4s %40s %12s" % ("#", "Term", "Coeff"))
for si in range(len(ansatz_ops)):
s = ansatz_ops[si]
opstring = ""
for t in s.terms:
opstring += str(t)
break
print(" %4i %40s %12.8f" % (si, opstring, parameters[si]))
return
# }}}
if __name__ == "__main__":
r = 1.5
#geometry = [('H', (0,0,1*r)), ('H', (0,0,2*r)), ('H', (0,0,3*r)), ('H', (0,0,4*r))]
geometry = [('H', (0, 0, 0)), ('Li', (0, 0, r * 2.39))]
#geometry = [('H', (0,0,1*r)), ('H', (0,0,2*r)), ('H', (0,0,3*r)), ('H', (0,0,4*r)), ('H', (0,0,5*r)), ('H', (0,0,6*r))]
#vqe_methods.ucc(geometry,pool = operator_pools.singlet_SD())
#vqe_methods.adapt_vqe(geometry,pool = operator_pools.singlet_SD())
#vqe_methods.adapt_vqe(geometry,pool = operator_pools.hamiltonian(), adapt_thresh=1e-7, theta_thresh=1e-8)
#vqe_methods.adapt_vqe(geometry,pool = operator_pools.singlet_SD(), adapt_thresh=1e-1, adapt_conver='uncertainty')
vqe_methods.adapt_vqe(geometry,
pool=operator_pools.spin_complement_GSD(),
adapt_thresh=1e-2,
theta_thresh=1e-9)
import vqe_methods
import operator_pools
r = 1.5
geometry = [('H', (0, 0, 1 * r)), ('H', (0, 0, 2 * r)), ('H', (0, 0, 3 * r)),
('H', (0, 0, 4 * r)), ('H', (0, 0, 5 * r)), ('H', (0, 0, 6 * r))]
vqe_methods.adapt_vqe(geometry, pool=operator_pools.singlet_SD())
reference_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(occupied_list,
2 * n_orb)).transpose()
[e, v] = scipy.sparse.linalg.eigsh(hamiltonian.real,
1,
which='SA',
v0=reference_ket.todense())
for ei in range(len(e)):
S2 = v[:, ei].conj().T.dot(s2.dot(v[:, ei]))
print(" State %4i: %12.8f au <S2>: %12.8f" % (ei, e[ei] + E_nuc, S2))
fermi_ham += FermionOperator((), E_nuc)
pyscf.molden.from_mo(mol, "full.molden", sq_ham.C)
pool = operator_pools.singlet_SD()
pool.init(n_orb,
n_occ_a=n_a,
n_occ_b=n_b,
n_vir_a=n_orb - n_a,
n_vir_b=n_orb - n_b)
[e, v, params] = vqe_methods.adapt_vqe(fermi_ham,
pool,
reference_ket,
theta_thresh=1e-9)
# [e,v,params] = vqe_methods.seqGO(fermi_ham, pool, reference_ket, theta_thresh=1e-9)
print(" Final ADAPT-VQE energy: %12.8f" % e)
print(" <S^2> of final state : %12.8f" %
(v.conj().T.dot(s2.dot(v))[0, 0].real))