Purification-based quantum error mitigation of pair-correlated electron simulations

ORAL

Abstract

An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Prior to fault-tolerant quantum computing, robust error mitigation strategies are necessary to continue this growth. Here, we study physical simulation within the seniority-zero electron pairing subspace, which affords both a computational stepping stone to a fully correlated model, and an opportunity to validate recently introduced ``purification-based'' error-mitigation strategies. We compare the performance of error mitigation based on doubling quantum resources in time (echo verification) or in space (virtual distillation), on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques (e.g. post-selection); the gain from error mitigation is seen to increase with the system size. Employing these error mitigation strategies enables the implementation of the largest variational algorithm for a correlated chemistry system to-date. Extrapolating performance from these results allows us to estimate minimum requirements for a beyond-classical simulation of electronic structure. We find that, despite the impressive gains from purification-based error mitigation, significant hardware improvements will be required for classically intractable variational chemistry simulations.

Publication: This work will appear as a paper on the arXiv tomorrow.

Presenters

  • Thomas E O'Brien

    • Google LLC

Authors

  • Thomas E O'Brien

    • Google LLC
  • Gian-Luca Anselmetti

    • Covestro
  • Fotios Gkritsis

    • Covestro
  • Vincent E Elfving

    • PASQAL SAS
    • Qu&Co
  • Stefano Polla

    • Lorentz Institute
  • William J Huggins

    • Google
    • Google Quantum AI
  • Oumarou Oumarou

    • Covestro
  • Kostyantyn Kechedzhi

    • Google LLC
    • Google
    • Google Quantum AI
  • Christian Gogolin

    • Covestro
  • Ryan Babbush

    • Google
  • Nicholas C Rubin

    • Google