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 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.
For the solution of electronic structure problems, the problem Hamiltonian is first expressed in the second quantization formalism, comprising fermionic excitation and annihilation operators. These can then be mapped to the qubit formalism using a variety of mappings such as Jordan-Wigner, Parity, and more, in readiness for the quantum computation.
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
- PyQuante, a pure cross-platform open-source Python chemistry program
- PySCF, an open-source Python chemistry program
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.settings import settings
from qiskit_nature.drivers import UnitsType
from qiskit_nature.drivers.second_quantization import PySCFDriver
from qiskit_nature.problems.second_quantization.electronic import ElectronicStructureProblem
settings.dict_aux_operators = True
# 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['ElectronicEnergy']
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 ElectronicStructureProblem.
This problem instance generates a list of second-quantized operators which we can map 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.
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.
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.