# Run this in a Jupyter notebook with qiskit installed from qiskit_nature.units import DistanceUnit from qiskit_nature.second_q.drivers import PySCFDriver from qiskit_nature.second_q.mappers import ParityMapper from qiskit_nature.second_q.algorithms import GroundStateEigensolver from qiskit_algorithms import NumPyMinimumEigensolver
Quantum chemistry and computing are merging into – a field that will likely transform how we discover molecules. You don’t need a PhD to start understanding it. Play with the free simulators, watch the summer school lectures, and remember: every quantum algorithm for chemistry began with someone being curious about a tiny molecule. quantum chemistry and computing for the curious read online
The electronic Hamiltonian (\hatH) (which describes kinetic + potential energy) is rewritten as a sum of Pauli operators ((X, Y, Z)) acting on qubits. # Run this in a Jupyter notebook with
We are currently in the "Wright Brothers" phase of quantum chemistry. The "flights" are short and the machines are fragile, but we know the physics works. For the curious reader, the next decade will be a front-row seat to a revolution in how we understand the very fabric of matter. For the curious reader, the next decade will
driver = PySCFDriver(atom="H .0 .0 .0; H .0 .0 0.74", basis="sto3g", unit=DistanceUnit.ANGSTROM) problem = driver.run() mapper = ParityMapper() solver = NumPyMinimumEigensolver() # exact classical solver gse = GroundStateEigensolver(mapper, solver) result = gse.solve(problem) print(result.total_energies)