def opdm_generator(self, parameters):
opdm, var_dict = self.blackbox.calculate_rdm(parameters)
self.noisy_opdms.append(opdm)
self.variance_dicts.append(var_dict)
if self.purification:
opdm = cca.mcweeny_purification(opdm)
return opdm
def test_mcweeny():
np.random.seed(82)
opdm = np.array([[
0.766034130, -0.27166330, -0.30936072, -0.08471057, -0.04878244,
-0.01285432
],
[
-0.27166330, 0.67657015, -0.37519640, -0.02101843,
-0.03568214, -0.05034585
],
[
-0.30936072, -0.37519640, 0.55896791, 0.04267370,
-0.02258184, -0.08783738
],
[
-0.08471057, -0.02101843, 0.04267370, 0.05450848,
0.11291253, 0.17131658
],
[
-0.04878244, -0.03568214, -0.02258184, 0.11291253,
0.26821219, 0.42351185
],
[
-0.01285432, -0.05034585, -0.08783738, 0.17131658,
0.42351185, 0.67570713
]])
for i, j in product(range(6), repeat=2):
opdm[i, j] += np.random.randn() * 1.0E-3
opdm = 0.5 * (opdm + opdm.T)
pure_opdm = mcweeny_purification(opdm)
w, _ = np.linalg.eigh(pure_opdm)
assert len(np.where(w < -1.0E-9)[0]) == 0
def calculate_rdm(self, parameters):
data_dict = self.calculate_data(parameters)
opdm, opdm_var_dict = cca.compute_opdm(data_dict, return_variance=True)
if self.purification:
self._last_noisy_opdm = opdm
opdm = cca.mcweeny_purification(opdm)
return opdm, opdm_var_dict
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