def test_opdm_func_vals():
# coverage: ignore
rhf_objective, molecule, parameters, obi, tbi = make_h6_1_3()
qubits = [cirq.GridQubit(0, x) for x in range(molecule.n_orbitals)]
np.random.seed(43)
sampler = cirq.Simulator(dtype=np.complex128)
opdm_func = OpdmFunctional(qubits=qubits,
sampler=sampler,
constant=molecule.nuclear_repulsion,
one_body_integrals=obi,
two_body_integrals=tbi,
num_electrons=molecule.n_electrons // 2)
assert isinstance(opdm_func, OpdmFunctional)
data = opdm_func.calculate_data(parameters)
assert isinstance(data, dict)
assert list(data.keys()) == [
'z', 'xy_even', 'xy_odd', 'qubits', 'qubit_permutations', 'circuits',
'circuits_with_measurement'
]
opdm_from_data = compute_opdm(data, return_variance=False)
opdm_from_obj, var_dict = opdm_func.calculate_rdm(parameters)
assert isinstance(var_dict, dict)
assert np.linalg.norm(opdm_from_data - opdm_from_obj) < 1.0E-2
assert np.isclose(opdm_func.energy_from_opdm(opdm_from_data),
rhf_objective.energy_from_opdm(opdm_from_data))
rdm_gen = RDMGenerator(opdm_func)
rdm_gen.opdm_generator(parameters)
assert len(rdm_gen.noisy_opdms) == 1
assert len(rdm_gen.variance_dicts) == 1
def test_energy_from_opdm():
"""Build test assuming sampling functions work"""
rhf_objective, molecule, parameters, obi, tbi = make_h6_1_3()
unitary, energy, _ = rhf_func_generator(rhf_objective)
parameters = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
initial_opdm = np.diag([1] * 3 + [0] * 3)
final_opdm = unitary(parameters) @ initial_opdm @ unitary(
parameters).conj().T
test_energy = energy_from_opdm(final_opdm,
constant=molecule.nuclear_repulsion,
one_body_tensor=obi,
two_body_tensor=tbi)
true_energy = energy(parameters)
assert np.allclose(test_energy, true_energy)
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)
def test_global_gradient_h4():
"""
Test the gradient at the solution given by psi4
"""
# first get molecule
rhf_objective, molecule, params, _, _ = make_h6_1_3()
# molecule = h4_linear_molecule(1.0)
nocc = molecule.n_electrons // 2
occ = list(range(nocc))
virt = list(range(nocc, molecule.n_orbitals))
qubits = cirq.LineQubit.range(molecule.n_orbitals)
u = sp.linalg.expm(
rhf_params_to_matrix(params, len(qubits), occ=occ, virt=virt))
circuit = cirq.Circuit(prepare_slater_determinant(qubits, u[:, :nocc].T))
simulator = cirq.Simulator(dtype=np.complex128)
wf = simulator.simulate(circuit).final_state.reshape((-1, 1))
opdm_alpha = get_opdm(wf, molecule.n_orbitals)
opdm = np.zeros((molecule.n_qubits, molecule.n_qubits),
dtype=np.complex128)
opdm[::2, ::2] = opdm_alpha
opdm[1::2, 1::2] = opdm_alpha
grad = rhf_objective.global_gradient_opdm(params, opdm_alpha)
# get finite difference gradient
finite_diff_grad = np.zeros(9)
epsilon = 0.0001
for i in range(9):
params_epsilon = params.copy()
params_epsilon[i] += epsilon
u = sp.linalg.expm(
rhf_params_to_matrix(params_epsilon,
len(qubits),
occ=occ,
virt=virt))
circuit = cirq.Circuit(
prepare_slater_determinant(qubits, u[:, :nocc].T))
wf = simulator.simulate(circuit).final_state.reshape((-1, 1))
opdm_pepsilon = get_opdm(wf, molecule.n_orbitals)
energy_plus_epsilon = rhf_objective.energy_from_opdm(opdm_pepsilon)
params_epsilon[i] -= 2 * epsilon
u = sp.linalg.expm(
rhf_params_to_matrix(params_epsilon,
len(qubits),
occ=occ,
virt=virt))
circuit = cirq.Circuit(
prepare_slater_determinant(qubits, u[:, :nocc].T))
wf = simulator.simulate(circuit).final_state.reshape((-1, 1))
opdm_pepsilon = get_opdm(wf, molecule.n_orbitals)
energy_minus_epsilon = rhf_objective.energy_from_opdm(opdm_pepsilon)
finite_diff_grad[i] = (energy_plus_epsilon -
energy_minus_epsilon) / (2 * epsilon)
assert np.allclose(finite_diff_grad, grad, atol=epsilon)
# random parameters now
params = np.random.randn(9)
u = sp.linalg.expm(
rhf_params_to_matrix(params, len(qubits), occ=occ, virt=virt))
circuit = cirq.Circuit(prepare_slater_determinant(qubits, u[:, :nocc].T))
simulator = cirq.Simulator(dtype=np.complex128)
wf = simulator.simulate(circuit).final_state.reshape((-1, 1))
opdm_alpha = get_opdm(wf, molecule.n_orbitals)
opdm = np.zeros((molecule.n_qubits, molecule.n_qubits),
dtype=np.complex128)
opdm[::2, ::2] = opdm_alpha
opdm[1::2, 1::2] = opdm_alpha
grad = rhf_objective.global_gradient_opdm(params, opdm_alpha)
# get finite difference gradient
finite_diff_grad = np.zeros(9)
epsilon = 0.0001
for i in range(9):
params_epsilon = params.copy()
params_epsilon[i] += epsilon
u = sp.linalg.expm(
rhf_params_to_matrix(params_epsilon,
len(qubits),
occ=occ,
virt=virt))
circuit = cirq.Circuit(
prepare_slater_determinant(qubits, u[:, :nocc].T))
wf = simulator.simulate(circuit).final_state.reshape((-1, 1))
opdm_pepsilon = get_opdm(wf, molecule.n_orbitals)
energy_plus_epsilon = rhf_objective.energy_from_opdm(opdm_pepsilon)
params_epsilon[i] -= 2 * epsilon
u = sp.linalg.expm(
rhf_params_to_matrix(params_epsilon,
len(qubits),
occ=occ,
virt=virt))
circuit = cirq.Circuit(
prepare_slater_determinant(qubits, u[:, :nocc].T))
wf = simulator.simulate(circuit).final_state.reshape((-1, 1))
opdm_pepsilon = get_opdm(wf, molecule.n_orbitals)
energy_minus_epsilon = rhf_objective.energy_from_opdm(opdm_pepsilon)
finite_diff_grad[i] = (energy_plus_epsilon -
energy_minus_epsilon) / (2 * epsilon)
assert np.allclose(finite_diff_grad, grad, atol=epsilon)