def helloh1():
rhf_objective, molecule, parameters, obi, tbi = make_h3_2_5()
ansatz, energy, gradient = rhf_func_generator(rhf_objective)
jsondict = {}
jsondict["comment"] = "Done"
jsondict["schema"] = "h3test-circuit-result"
return jsondict
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_energy_from_opdm():
"""Build test assiming 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_energy_from_opdm_odd_qubit():
"""Build test assuming sampling functions work"""
rhf_objective, molecule, parameters, obi, tbi = make_h3_2_5()
unitary, energy, _ = rhf_func_generator(rhf_objective)
parameters = np.array([0.1, 0.2])
initial_opdm = np.diag([1] * 1 + [0] * 2)
print(initial_opdm)
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 helloh3():
# Generate the input files, set up quantum resources, and set up the OpdmFunctional to make measurements.
rhf_objective, molecule, parameters, obi, tbi = make_h3_2_5()
ansatz, energy, gradient = rhf_func_generator(rhf_objective)
# settings for quantum resources
qubits = [cirq.GridQubit(0, x) for x in range(molecule.n_orbitals)]
sampler = cirq.Simulator(
dtype=np.complex128) # this can be a QuantumEngine
# OpdmFunctional contains an interface for running experiments
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, # only simulate spin-up electrons
clean_xxyy=True,
purification=True)
# Do measurements for a given set of parameters
measurement_data = opdm_func.calculate_data(parameters)
# Compute 1-RDM, variances, and purification
opdm, var_dict = compute_opdm(measurement_data, return_variance=True)
opdm_pure = mcweeny_purification(opdm)
# Compute energy, fidelities, and errorbars
raw_energies = []
raw_fidelity_witness = []
purified_energies = []
purified_fidelity_witness = []
purified_fidelity = []
true_unitary = ansatz(parameters)
nocc = molecule.n_electrons // 2
nvirt = molecule.n_orbitals - nocc
initial_fock_state = [1] * nocc + [0] * nvirt
for _ in range(1000): # 1000 repetitions of the measurement
new_opdm = resample_opdm(opdm, var_dict)
raw_energies.append(opdm_func.energy_from_opdm(new_opdm))
raw_fidelity_witness.append(
fidelity_witness(target_unitary=true_unitary,
omega=initial_fock_state,
measured_opdm=new_opdm))
# fix positivity and trace of sampled 1-RDM if strictly outside
# feasible set
w, v = np.linalg.eigh(new_opdm)
if len(np.where(w < 0)[0]) > 0:
new_opdm = fixed_trace_positive_projection(new_opdm, nocc)
new_opdm_pure = mcweeny_purification(new_opdm)
purified_energies.append(opdm_func.energy_from_opdm(new_opdm_pure))
purified_fidelity_witness.append(
fidelity_witness(target_unitary=true_unitary,
omega=initial_fock_state,
measured_opdm=new_opdm_pure))
purified_fidelity.append(
fidelity(target_unitary=true_unitary, measured_opdm=new_opdm_pure))
jsondict = {}
jsondict["canonicalHFEnergy"] = str(molecule.hf_energy)
jsondict["trueEnergy"] = str(energy(parameters))
jsondict["rawEnergy"] = str(opdm_func.energy_from_opdm(opdm))
jsondict["rawEnergySd"] = str(np.std(raw_energies))
jsondict["rawFidelityWitness"] = str(np.mean(raw_fidelity_witness))
jsondict["rawFidelityWitnessSd"] = str(np.std(raw_fidelity_witness))
jsondict["purifiedEnergy"] = str(opdm_func.energy_from_opdm(opdm_pure))
jsondict["purifiedEnergySd"] = str(np.std(purified_energies))
jsondict["purifiedFidelityWitness"] = str(
np.mean(purified_fidelity_witness).real)
jsondict["purifiedFidelityWitnessSd"] = str(
np.std(purified_fidelity_witness))
jsondict["purifiedFidelity"] = str(np.mean(purified_fidelity).real)
jsondict["purifiedFidelitySd"] = str(np.std(purified_fidelity))
jsondict["schema"] = "h3test-circuit-result"
return jsondict
# # now make a separate folder for each of 50 points along a line
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)