Qiskit Nature is an open-source framework which supports solving quantum mechanical natural science problems using quantum computing algorithms. This includes finding ground and excited states of electronic and vibrational structure problems, measuring the dipole moments of molecular systems, solving the Ising and Fermi-Hubbard models on lattices, and much more.
The code comprises various modules revolving around:
- data loading from chemistry drivers or file formats
- second-quantized operator construction and manipulation
- translating from the second-quantized to the qubit space
- a quantum circuit library of natural science targeted ansatze
- natural science specific algorithms and utilities to make the use of Qiskit Terra's algorithms easier
- and much more
We encourage installing Qiskit Nature via the pip tool (a python package manager).
pip install qiskit-naturepip 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.
- Gaussian 16™, a commercial chemistry program
- PSI4, a chemistry program that exposes a Python interface allowing for accessing internal objects
- PySCF, an open-source Python chemistry program
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.units import DistanceUnit
from qiskit_nature.second_q.drivers import PySCFDriver
# 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=DistanceUnit.ANGSTROM,
basis='sto3g',
)
problem = driver.run()
# setup the mapper and qubit converter
from qiskit_nature.second_q.mappers import ParityMapper
from qiskit_nature.second_q.mappers import QubitConverter
mapper = ParityMapper()
converter = QubitConverter(mapper=mapper, two_qubit_reduction=True)
# setup the classical optimizer for the VQE
from qiskit.algorithms.optimizers import L_BFGS_B
optimizer = L_BFGS_B()
# setup the estimator primitive for the VQE
from qiskit.primitives import Estimator
estimator = Estimator()
# setup the ansatz for VQE
from qiskit_nature.second_q.circuit.library import UCCSD
ansatz = UCCSD()
# use a factory to complement the VQE and its components at runtime
from qiskit_nature.second_q.algorithms import VQEUCCFactory
vqe_factory = VQEUCCFactory(estimator, ansatz, optimizer)
# prepare the ground-state solver and run it
from qiskit_nature.second_q.algorithms import GroundStateEigensolver
algorithm = GroundStateEigensolver(converter, vqe_factory)
electronic_structure_result = algorithm.solve(problem)
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 produces an ElectronicStructureProblem
which gathers all the problem information required by Qiskit Nature.
The second-quantized operators contained in that problem can be mapped to qubit operators with a
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.
For actually finding the ground state solution, the Variational Quantum Eigensolver (VQE) algorithm is used.
Its main three components, the estimator primitive, wavefunciton ansatz (UCCSD), and optimizer, are passed
to the VQEUCCFactory, a utility of Qiskit Nature simplifying the setup of the VQE algorithm and its
components. This factory also ensures consistent settings for the ansatzes initial state and the optimizers
initial point.
The entire problem is then solved using a GroundStateEigensolver which wraps both, the QubitConverter
and VQEUCCFactory. Since an ElectronicStructureProblem is provided to it (which was the output of the
PySCFDriver) it also returns an ElectronicStructureResult.
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.
If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.
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:
- The Gaussian 16 driver in
qiskit-naturecontains work licensed under the Gaussian Open-Source Public License.