Qiskit Nature is an open-source framework that supports problems including ground state energy computations, excited states and dipole moments of molecule, both open and closed-shell.
The code comprises chemistry drivers, which when provided with a molecular configuration will return one and two-body integrals as well as other data that is efficiently computed classically. This output data from a driver can then be used as input in Qiskit Nature that contains logic which is able to translate this into a form that is suitable for quantum algorithms. The conversion first creates a FermionicOperator which must then be mapped, e.g. by a Jordan Wigner mapping, to a qubit operator in readiness for the quantum computation.
We encourage installing Qiskit Nature via the pip tool (a python package manager).
pip install qiskit-nature
pip will handle all dependencies automatically and you will always install the latest (and well-tested) version.
If you want to work on the very latest work-in-progress versions, either to try features ahead of their official release or if you want to contribute to Qiskit Nature, then you can install from source. To do this follow the instructions in the documentation.
To run chemistry experiments using Qiskit Nature, it is recommended that you install a classical computation chemistry software program/library interfaced by Qiskit. Several, as listed below, are supported, and while logic to interface these programs is supplied by Qiskit Nature via the above pip installation, the dependent programs/libraries themselves need to be installed separately.
A useful functionality integrated into Qiskit Nature is its ability to serialize a file in hierarchical Data Format 5 (HDF5) format representing all the output data from a chemistry driver.
The HDF5 driver accepts such HDF5 files as input so molecular experiments can be run, albeit on the fixed data as stored in the file. As such, if you have some pre-created HDF5 files created from Qiskit Nature, you can use these with the HDF5 driver even if you do not install one of the classical computation packages listed above.
Now that Qiskit Nature is installed, let's try a chemistry application experiment using the VQE (Variational Quantum Eigensolver) algorithm to compute the ground-state (minimum) energy of a molecule.
from qiskit_nature.drivers import PySCFDriver, UnitsType from qiskit_nature.problems.second_quantization.electronic import ElectronicStructureProblem # Use PySCF, a classical computational chemistry software # package, to compute the one-body and two-body integrals in # electronic-orbital basis, necessary to form the Fermionic operator driver = PySCFDriver(atom='H .0 .0 .0; H .0 .0 0.735', unit=UnitsType.ANGSTROM, basis='sto3g') problem = ElectronicStructureProblem(driver) # generate the second-quantized operators second_q_ops = problem.second_q_ops() main_op = second_q_ops particle_number = problem.grouped_property_transformed.get_property("ParticleNumber") num_particles = (particle_number.num_alpha, particle_number.num_beta) num_spin_orbitals = particle_number.num_spin_orbitals # setup the classical optimizer for VQE from qiskit.algorithms.optimizers import L_BFGS_B optimizer = L_BFGS_B() # setup the mapper and qubit converter from qiskit_nature.mappers.second_quantization import ParityMapper from qiskit_nature.converters.second_quantization import QubitConverter mapper = ParityMapper() converter = QubitConverter(mapper=mapper, two_qubit_reduction=True) # map to qubit operators qubit_op = converter.convert(main_op, num_particles=num_particles) # setup the initial state for the ansatz from qiskit_nature.circuit.library import HartreeFock init_state = HartreeFock(num_spin_orbitals, num_particles, converter) # setup the ansatz for VQE from qiskit.circuit.library import TwoLocal ansatz = TwoLocal(num_spin_orbitals, ['ry', 'rz'], 'cz') # add the initial state ansatz.compose(init_state, front=True, inplace=True) # set the backend for the quantum computation from qiskit import Aer backend = Aer.get_backend('aer_simulator_statevector') # setup and run VQE from qiskit.algorithms import VQE algorithm = VQE(ansatz, optimizer=optimizer, quantum_instance=backend) result = algorithm.compute_minimum_eigenvalue(qubit_op) print(result.eigenvalue.real) electronic_structure_result = problem.interpret(result) print(electronic_structure_result)
The program above uses a quantum computer to calculate the ground state energy of molecular Hydrogen,
H2, where the two atoms are configured to be at a distance of 0.735 angstroms. The molecular
input specification is processed by the PySCF driver. This driver is wrapped by the
This problem instance generates a list of second-quantized operators which we can map to qubit operators
QubitConverter. Here, we chose the parity mapping in combination with a 2-qubit reduction, which
is a precision-preserving optimization removing two qubits; a reduction in complexity that is particularly
advantageous for NISQ computers.
The qubit operator is then passed as an input to the Variational Quantum Eigensolver (VQE) algorithm,
instantiated with a classical optimizer and a RyRz ansatz (
TwoLocal). A Hartree-Fock initial state
is used as a starting point for the ansatz.
The VQE algorithm is then run, in this case on the Qiskit Aer statevector simulator backend.
Here we pass a backend but it can be wrapped into a
QuantumInstance, and that passed to the
run instead. The
QuantumInstance API allows you to customize run-time properties of the backend,
such as the number of shots, the maximum number of credits to use, settings for the simulator,
initial layout of qubits in the mapping and the Terra
PassManager that will handle the compilation
of the circuits. By passing in a backend as is done above it is internally wrapped into a
QuantumInstance and is a convenience when default setting suffice.
In the end, you are given a result object by the VQE which you can analyze further by interpreting it with your problem instance.
Learning path notebooks may be found in the Nature Tutorials section of the documentation and are a great place to start
Jupyter notebooks containing further Nature examples may be found in the following Qiskit GitHub repositories at qiskit-nature/docs/tutorials.
We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.
Qiskit Nature was inspired, authored and brought about by the collective work of a team of researchers. Qiskit Nature continues to grow with the help and work of many people, who contribute to the project at different levels. If you use Qiskit, please cite as per the provided BibTeX file.
Please note that if you do not like the way your name is cited in the BibTex file then consult the information found in the .mailmap file.
This project uses the Apache License 2.0.
However there is some code that is included under other licensing as follows: