Calculating the Dissociation Curve of Molecular Nitrogen on a Superconducting Quantum Computer

ORAL

Abstract

The contextual subspace variational quantum eigensolver (CS-VQE) is a hybrid quantum-classical algorithm that approximates the ground-state energy of a given Hamiltonian. In this work we present an experimental demonstration of the CS-VQE algorithm on superconducting quantum hardware. We compute the potential energy curve for molecular nitrogen, where a dominance of static correlation in the dissociation limit proves challenging for many conventional quantum chemistry techniques. The CS-VQE methodology is competitive with standard subspace methods used in quantum chemistry, but at a considerable saving of quantum resource, meaning different active spaces can be treated for a fixed qubit allowance.

*T.W. and A.R. acknowledge support from the Unitary Fund. T.W. also acknowledges support from EPSRC (EP/S021582/1), CBKSciCon Ltd., Atos, Intel and Zapata. A.R. and P.J.L. acknowledges support by the NSF STAQ project (PHY-1818914). S.S. wishes to acknowledge financial support from the National Centre for HPC, Big Data and Quantum Computing (Spoke 10,CN00000013). P.V.C. is grateful for funding from the European Commission for VECMA (800925) and EPSRC for SEAVEA (EP/W007711/1). Access to the IBM Quantum Computers was obtained through the IBM Quantum Hub at CERN with which the Italian Institute of Technology (IIT) is affiliated.

Publication: Tim Weaving, Alexis Ralli, Peter J. Love, Sauro Succi, & Peter V. Coveney, Contextual Subspace Variational Quantum Eigensolver Calculation of the Dissociation Curve of Molecular Nitrogen on a Superconducting Quantum Computer, arXiv preprint arXiv:2312.04392 (2024)

Presenters

  • Alexis P Ralli

    • Tufts University

Authors

  • Alexis P Ralli

    • Tufts University
  • Tim Weaving

    • University College London
  • Peter J Love

    • Tufts University
  • Sauro Succi

    • IAC/NRC
    • Italian Institute of Technology
  • Peter V Coveney

    • University College London