Running quantum-classical algorithms via qc2Data class

As key component of qc2, the qc2Data class is designed not only to smoothly connect with qc2-ASE calculators (see Running qc2-ASE calculators via qc2Data class) but also to seamlessly integrate with its built-in package of native of Algorithms. This is precisely the goal of qc2, and it is what users should expect in actual qc2 runs.

This connection is facilitated through the qc2Data’s algorithm attribute. Below, we present examples of complete hybrid quantum-classical runs using qc2. For more detailed guidance, users are referred to the Tutorials section and examples directory.

In the first code snippet, we show how to perform a full VQE calculaton for hydrogen molecule. This uses fcidump as qchem data schema and Qiskit-Nature with the SLSQP optimizer, qiskit.Estimator and Bravyi-Kitaev mapper.

from ase.build import molecule

from qiskit_algorithms.optimizers import SLSQP
from qiskit.primitives import Estimator

from qc2.data import qc2Data
from qc2.ase import PySCF
from qc2.algorithms.qiskit import VQE
from qc2.algorithms.utils import ActiveSpace

# set ASE Atoms object
mol = molecule('H2')

# attach a qc2-ASE calculator to Atoms object
mol.calc = PySCF()  # => defaults to HF/sto-3g

# instantiate qc2Data class
qc2data = qc2Data(
    molecule=mol,
    filename='h2.fcidump',
    schema='fcidump'
)

# run qc2-ASE calculator
qc2data.run()

# instantiate VQE algorithm class
qc2data.algorithm = VQE(
    active_space=ActiveSpace(
        num_active_electrons=(1, 1),
        num_active_spatial_orbitals=2
    ),
    mapper="bk",
    optimizer=SLSQP(),
    estimator=Estimator(),
)

# run vqe
results = qc2data.algorithm.run()

The second example shows a oo-VQE run for water and PennyLane. This now uses qcschema as data format with the qc2-ASE Psi4 calculator. Extra options for PennyLane’s device and QNode are also added.

from ase.build import molecule

import pennylane as qml

from qc2.data import qc2Data
from qc2.ase import Psi4
from qc2.algorithms.pennylane import oo_VQE
from qc2.algorithms.utils import ActiveSpace

# set ASE Atoms object
mol = molecule('H2O')

# instantiate qc2Data class
qc2data = qc2Data(
    molecule=mol,
    filename='h2o.hdf5',
    schema='qcschema'
)

# one can also attach qc2-ASE calculator later on to the molecule attribute
qc2data.molecule.calc = Psi4(method="hf", basis="sto-3g")

# run qc2-ASE calculator
qc2data.run()

# instantiate oo-VQE class
qc2data.algorithm = oo_VQE(
    active_space=ActiveSpace(
        num_active_electrons=(2, 2),
        num_active_spatial_orbitals=4
    ),
    mapper='jw',
    optimizer=qml.GradientDescentOptimizer(stepsize=0.5),
    device='default.qubit'
)

# run oo-VQE algorithm with special device and QNode options if needed
results = qc2data.algorithm.run(
    device_kwargs={"shots": None},
    qnode_kwargs={"diff_method": "best"}
)

where results in both cases correspond to instances of OOVQEResults and VQEResults classes, respectively.