from qucochemistry.vqe import VQEexperiment from openfermion.hamiltonians import MolecularData
filename = 'molecules/H2_pyscf_equi.hdf5'
molecule = MolecularData(filename=filename)
vqe = VQEexperiment(molecule=molecule)
E = vqe.get_exact_gs()
vqe.start_vqe()
result = vqe.get_results()
print('Difference between VQE optimized and exact GS energy:')
print(str((result.fun-E)/0.0016) + 'kcal/mol')
dr. Oleksandr Kyriienko, University of Exeter
In this work we review the current status, and potential future outlook, of quantum hardware and algorithm theory in the field of quantum chemistry simulations. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. As particular examples of the resources and timings associated with classical and quantum computer simulations, we compare the molecules H_{2} for increasing basis set sizes, and Cr_{2} for a variety of complete active spaces (CAS) and simulated to chemical accuracy within that orbital set. The results enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.
This manuscript materialized from the collaboration between Schrödinger and Qu & Co.
One hallmark problem in computational linear algebra is the binary linear least squares (BLLS), which is formally in the NP-Hard complexity class. Efficient classical methods for solving this problem exists with limited approximations to the solution. Quantum computing may solve these problems with a better approximation ratio and/or in a faster runtime scaling. So-far, this problem has only been considered on a quantum annealing by mapping it to a QUBO. In this paper, the problem is solved using a QAOA approach on the gate-based model of quantum computing. The performance is assessed both on a wavefunction simulator, shotnoise simulator and on the 5-qubit IBM cloud computing quantum device ibmq_london. As an outlook: BLLS may serve as a building block for other problems such as Non-negative Binary Matrix Factorization, or clubbed together for a fixed-point approximation of real variables.
This project was partially supervised by Vincent Elfving from Qu & Co.
Accurate quantum chemistry simulations remain challenging on classical computers for problems of industrially relevant sizes and there is reason for hope that quantum computing may help push the boundaries of what is technically feasible. We in this research combine the so-called paired-electron approximation with techniques for simulating molecular chemistry on gate-based quantum computers and obtained a much more resource efficient algorithm, with little accuracy loss. We require half as many qubits, or conversely can increase the considered basis set size, which in turn leads to more accurate results together with reductions in the necessary number of quantum computing runs (shots) by several orders of magnitude, with runtime estimates and coherence requirements favourable to NISQ implementation.
This manuscript describes results from an ongoing collaboration between Covestro and Qu&Co
from qucochemistry.vqe import VQEexperiment from openfermion.hamiltonians import MolecularData
filename = 'molecules/H2_pyscf_equi.hdf5'
molecule = MolecularData(filename=filename)
vqe = VQEexperiment(molecule=molecule)
E = vqe.get_exact_gs()
vqe.start_vqe()
result = vqe.get_results()
print('Difference between VQE optimized and exact GS energy:')
print(str((result.fun-E)/0.0016) + 'kcal/mol')
dr. Jose Gamez, Covestro