def load_and_encode(file_name: str,
problem_description_index: int = 0,
initial_state_label: str = None) -> JWEncodedData:
"""Wrapper function for loading and encoding Broombridge file into
JWEncodedData-compatible format.
:param file_name: Broombridge file name
:type file_name: str
:param problem_description_index: Index of problem description to use,
defaults to 0
:type problem_description_index: int, optional
:param initial_state_label: Label of initial state to use, defaults to
first available label
:type initial_state_label: str, optional
"""
broombridge_data = load_broombridge(file_name)
problem = broombridge_data.problem_description[problem_description_index]
if initial_state_label is None:
# Pick first in list
initial_state_label = problem.initial_state_suggestions[0].get("Label")
_log.info(f"Using initial state label: {initial_state_label}")
input_state = load_input_state(file_name, initial_state_label)
ferm_hamiltonian = problem.load_fermion_hamiltonian()
(num_qubits, hamiltonian_term_list, input_state_terms,
energy_offset) = encode(ferm_hamiltonian, input_state)
return (num_qubits, hamiltonian_term_list, input_state_terms,
energy_offset)
def load_jw_encoded_data(filename: str):
broombridge_data = load_broombridge(filename)
problem_description = broombridge_data.problem_description[0]
ferm_hamiltonian = problem_description.load_fermion_hamiltonian()
input_state = load_input_state(filename, "UCCSD |G>")
jw_encoded = encode(ferm_hamiltonian, input_state)
return jw_encoded
def test_load_input_state():
"""
Checks that loading an input state from file or from broombridge gives the same result.
"""
broombridge = qsharp.chemistry.load_broombridge("broombridge.yaml")
is1 = broombridge.problem_description[0].load_input_state("UCCSD |G>")
is2 = load_input_state("broombridge.yaml", "UCCSD |G>")
assert(is1.Method == "UnitaryCoupledCluster")
assert(is1 == is2)
is3 = broombridge.problem_description[0].load_input_state("UCCSD |G>", IndexConvention.HalfUp)
is4 = load_input_state("broombridge.yaml", "UCCSD |G>", IndexConvention.HalfUp)
assert(is3.Method == "UnitaryCoupledCluster")
assert(is3 == is4)
assert(is1 != is4)
def test_load_greedy_state():
"""
Checks that not passing a wavefunction label generates the greedy input state.
"""
broombridge = qsharp.chemistry.load_broombridge("broombridge.yaml")
is1 = broombridge.problem_description[0].load_input_state("", IndexConvention.HalfUp)
is2 = load_input_state("broombridge.yaml", "", IndexConvention.HalfUp)
assert(is1.Method == "SparseMultiConfigurational")
assert(is1 == is2)
def __init__(self, ansatz, molecule, mean_field=None, backend_options=None):
"""Initialize the settings for simulation.
If the mean field is not provided it is automatically calculated.
Args:
ansatz (OpenFermionParametricSolver.Ansatze): Ansatz for the quantum solver.
molecule (pyscf.gto.Mole): The molecule to simulate.
mean_field (pyscf.scf.RHF): The mean field of the molecule.
backend_options (dict): Extra parameters that control the behaviour
of the solver.
"""
# Import python packages for Microsoft Python interops
import qsharp
import qsharp.chemistry as qsharpchem
assert(isinstance(ansatz, MicrosoftQSharpParametricSolver.Ansatze))
self.ansatz = ansatz
self.verbose = False
# Initialize the number of samples to be used by the MicrosoftQSharp backend
self.n_samples = 1e18
# Initialize the amplitudes (parameters to be optimized)
self.optimized_amplitudes = []
# Obtain fragment info with PySCF
# -----------------------------------------
# Compute mean-field if not provided. Check that it has converged
if not mean_field:
mean_field = scf.RHF(molecule)
mean_field.verbose = 0
mean_field.scf()
if (mean_field.converged == False):
orb_temp = mean_field.mo_coeff
occ_temp = mean_field.mo_occ
nr = scf.newton(mean_field)
energy = nr.kernel(orb_temp, occ_temp)
mean_field = nr
if not mean_field.converged:
warnings.warn("MicrosoftQSharpParametricSolver simulating with mean field not converged.",
RuntimeWarning)
self.molecule = molecule
self.mean_field = mean_field
self.n_orbitals = len(mean_field.mo_energy)
self.n_spin_orbitals = 2 * self.n_orbitals
self.n_electrons = molecule.nelectron
nuclear_repulsion = mean_field.energy_nuc()
# Compute and set values of electronic integrals
# ----------------------------------------------
# Get data-structure to store problem description
fd, path = tempfile.mkstemp(suffix='.yaml')
try:
# Write the dummp_0.2.yaml file to a temporary file
with os.fdopen(fd, 'w') as tmp:
tmp.write(_dummy_0_2_yaml)
molecular_data = qsharpchem.load_broombridge(path)
# Compute one and two-electron integrals, store them in the Microsoft data-structure
integrals_one, integrals_two = compute_integrals_fragment(molecule, mean_field)
molecular_data.problem_description[0].hamiltonian['OneElectronIntegrals']['Values'] = integrals_one
molecular_data.problem_description[0].hamiltonian['TwoElectronIntegrals']['Values'] = integrals_two
molecular_data.problem_description[0].coulomb_repulsion['Value'] = nuclear_repulsion
# Compute and set values of UCCSD operators
# -----------------------------------------
# Generate UCCSD one- and two-body operators
n_amplitudes = count_amplitudes(self.n_spin_orbitals, self.n_electrons)
self.amplitude_dimension = n_amplitudes
amplitudes = 0.01 * np.ones((n_amplitudes), dtype=np.float64)
self.preferred_var_params = amplitudes
ref,t = compute_cluster_operator(self.n_spin_orbitals, self.n_electrons, amplitudes)
# Load a dummy inputstate object from the dummy Broombridge file, and set its values
self.inputstate = qsharpchem.load_input_state(path, "UCCSD |G>")
if self.verbose:
print("inputstate energy :\n", self.inputstate.Energy)
print("inputstate mcfdata :\n", self.inputstate.MCFData)
print("inputstate method :\n", self.inputstate.Method)
print("inputstate scfdata :\n", self.inputstate.SCFData)
print("inputstate uccdata :\n", self.inputstate.UCCData, "\n\n\n")
self.inputstate.UCCData['Reference'] = ref
self.inputstate.UCCData['Excitations'] = t
if self.verbose:
print("inputstate :\n", self.inputstate.UCCData)
print("------------\n")
# Generate Fermionic and then qubit Hamiltonians
# ----------------------------------------------
# C# Chemistry library : Compute fermionic Hamiltonian
self.ferm_hamiltonian = molecular_data.problem_description[0].load_fermion_hamiltonian()
if self.verbose:
print("ferm_hamiltonian:\n", self.ferm_hamiltonian.terms)
print("------------\n")
# C# Chemistry library : Compute the Pauli Hamiltonian using the Jordan-Wigner transform
self.jw_hamiltonian = qsharpchem.encode(self.ferm_hamiltonian, self.inputstate)
if self.verbose:
print("jw_hamiltonian ::", self.jw_hamiltonian)
print("------------\n")
# Retrieve energy offset and number of qubits
self.n_qubits = self.jw_hamiltonian[0]
self.energy_offset = self.jw_hamiltonian[3]
finally:
# Cleanup the temp file
os.remove(path)
# optionally, load first the problem description from a Broombridge file, and from there
# load the fermion Hamiltonian from a problem description:
broombridge = load_broombridge("broombridge.yaml")
fh2 = broombridge.problem_description[0].load_fermion_hamiltonian()
print("fh2 ready.")
logging.info(fh2.terms)
# Here I show how to add a couple of completely made-up terms to the fh2 Hamiltonian:
terms = [([], 10.0), ([0, 6], 1.0), ([0, 2, 2, 0], 1.0)]
fh2.add_terms(terms)
print("terms added successfully")
logging.info(fh2)
# Similarly, you can load an input state either directly from a broombridge.yaml file,
is1 = load_input_state("broombridge.yaml", "|E1>")
print("is1 ready.")
logging.info(is1)
# or from the problem description:
is2 = broombridge.problem_description[0].load_input_state(
"|E1>", index_convention=IndexConvention.HalfUp)
print("is2 ready.")
logging.info(is2)
####
# An end-to-end example of how to simulate H2:
####
# Reload to make sure the quantum.qs file is correctly compiled:
qsharp.reload()
