GALIC: Hybrid Multi-Qubitwise Pauli Grouping for Quantum Computing Measurement

ORAL

Abstract

Observable estimation is a core primitive in NISQ-era algorithms. Two primary grouping schemes for simultaneous Pauli measurements have been proposed: fully commutativity (FC) and qubit-wise commutativity (QWC), with no compelling means of interpolation. In this work, we propose a generalized framework for designing and analyzing context-aware hybrid FC/QWC commutativity relations. We use our framework to propose a noise-and-connectivity-aware grouping strategy: Generalized backend-Aware pauLI Commutation (GALIC). We demonstrate how GALIC interpolates between FC and QWC, maintaining estimator accuracy in Hamiltonian estimation while lowering variance by an average of 20% compared to QWC. We also explore the design space of near-term quantum devices using the GALIC framework, specifically comparing device noise levels and connectivity. We find that error suppression has a more than 13x larger impact on device-aware estimator variance than qubit connectivity with even larger correlation differences in estimator biases.

*This work was supported by the "Embedding QC into Manybody Frameworks for Strongly Correlated Molecular and Materials Systems" project, which is funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (BES), the Division of Chemical Sciences, Geosciences, and Biosciences (under award 72689). This research used resources of the Oak Ridge Leadership Computing Facility (OLCF), which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231. The Pacific Northwest National Laboratory (PNNL) is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830.

Publication: arXiv:2409.00576

Presenters

  • Chenxu Liu

    • Pacific Northwest National Laboratory (PNNL)

Authors

  • Chenxu Liu

    • Pacific Northwest National Laboratory (PNNL)
  • Matthew X Burns

    • Pacific Northwest National Laboratory (PNNL); University of Rochester
  • Samuel A Stein

    • Pacific Northwest National Laboratory (PNNL)
  • Bo Peng

    • Pacific Northwest National Laboratory (PNNL)
  • Karol Kowalski

    • Pacific Northwest National Laboratory (PNNL)
  • Ang Li

    • Pacific Northwest National Laboratory (PNNL)