def make_h3_2_5() -> Tuple[RestrictedHartreeFockObjective, of.MolecularData,
np.ndarray, np.ndarray, np.ndarray]:
# load the molecule from moelcular data
h3_2_5_path = os.path.join(
hfvqe.__path__[0],
'molecular_data/hydrogen_chains/h_3_p_sto-3g/bond_distance_2.5')
molfile = os.path.join(h3_2_5_path,
'H3_plus_sto-3g_singlet_linear_r-2.5.hdf5')
molecule = of.MolecularData(filename=molfile)
molecule.load()
S = np.load(os.path.join(h3_2_5_path, 'overlap.npy'))
Hcore = np.load(os.path.join(h3_2_5_path, 'h_core.npy'))
TEI = np.load(os.path.join(h3_2_5_path, 'tei.npy'))
_, X = sp.linalg.eigh(Hcore, S)
obi = of.general_basis_change(Hcore, X, (1, 0))
tbi = np.einsum('psqr', of.general_basis_change(TEI, X, (1, 0, 1, 0)))
molecular_hamiltonian = generate_hamiltonian(obi, tbi,
molecule.nuclear_repulsion)
rhf_objective = RestrictedHartreeFockObjective(molecular_hamiltonian,
molecule.n_electrons)
scipy_result = rhf_minimization(rhf_objective)
return rhf_objective, molecule, scipy_result.x, obi, tbi
def make_h3_2_5(molecular_data_directory=None) \
-> Tuple[RestrictedHartreeFockObjective, of.MolecularData,
np.ndarray, np.ndarray, np.ndarray]:
if molecular_data_directory is None:
molecular_data_directory = _MOLECULAR_DATA_DIRECTORY
h3_2_5_path = f'{molecular_data_directory}/hydrogen_chains/h_3_p_sto-3g/bond_distance_2.5'
molfile = f'{h3_2_5_path}/H3_plus_sto-3g_singlet_linear_r-2.5.hdf5'
molecule = of.MolecularData(filename=molfile)
molecule.load()
S = np.load(os.path.join(h3_2_5_path, 'overlap.npy'))
Hcore = np.load(os.path.join(h3_2_5_path, 'h_core.npy'))
TEI = np.load(os.path.join(h3_2_5_path, 'tei.npy'))
_, X = sp.linalg.eigh(Hcore, S)
obi = of.general_basis_change(Hcore, X, (1, 0))
tbi = np.einsum('psqr', of.general_basis_change(TEI, X, (1, 0, 1, 0)))
molecular_hamiltonian = generate_hamiltonian(obi, tbi,
molecule.nuclear_repulsion)
rhf_objective = RestrictedHartreeFockObjective(molecular_hamiltonian,
molecule.n_electrons)
scipy_result = rhf_minimization(rhf_objective)
return rhf_objective, molecule, scipy_result.x, obi, tbi
def test_rhf_func_gen():
rhf_objective, molecule, parameters, _, _ = make_h6_1_3()
ansatz, energy, _ = rhf_func_generator(rhf_objective)
assert np.isclose(molecule.hf_energy, energy(parameters))
ansatz, energy, _, opdm_func = rhf_func_generator(rhf_objective,
initial_occ_vec=[1] * 3 +
[0] * 3,
get_opdm_func=True)
assert np.isclose(molecule.hf_energy, energy(parameters))
test_opdm = opdm_func(parameters)
u = ansatz(parameters)
initial_opdm = np.diag([1] * 3 + [0] * 3)
final_odpm = u @ initial_opdm @ u.T
assert np.allclose(test_opdm, final_odpm)
result = rhf_minimization(rhf_objective, initial_guess=parameters)
assert np.allclose(result.x, parameters)
bond_distances = numpy.linspace(0.5, 2.5, 6)
for bb in bond_distances:
print(bb)
local_dir = 'bond_distance_{:.1f}'.format(bb)
os.mkdir(local_dir)
os.chdir(local_dir)
molecule = molecule_generator[n](bb)
rhf_objective, S, HCore, TEI = make_rhf_objective(molecule)
numpy.save("overlap.npy", S)
numpy.save("h_core.npy", HCore)
numpy.save("tei.npy", TEI)
ansatz, energy, gradient = rhf_func_generator(rhf_objective)
scipy_result = rhf_minimization(rhf_objective)
print(molecule.hf_energy)
print(scipy_result.fun)
assert numpy.isclose(molecule.hf_energy, scipy_result.fun)
numpy.save("parameters.npy", numpy.asarray(scipy_result.x))
initial_opdm = numpy.diag([1] * rhf_objective.nocc +
[0] * rhf_objective.nvirt)
unitary = ansatz(scipy_result.x)
final_opdm = unitary @ initial_opdm @ numpy.conjugate(unitary).T
assert numpy.isclose(rhf_objective.energy_from_opdm(final_opdm),
scipy_result.fun)
numpy.save("true_opdm.npy", numpy.asarray(final_opdm))
molecule.filename = os.path.join(os.getcwd(), molecule.name)
molecule.save()