Quantum Replica Exchange

ORAL

Abstract

The presence of energy barriers in the state space of a physical system can lead to exponentially slow convergence for sampling algorithms like Markov chain Monte Carlo (MCMC). In the classical setting, replica exchange (or parallel tempering) is a powerful heuristic to accelerate mixing in these scenarios. In the quantum realm, preparing Gibbs states of Hamiltonians faces a similar challenge, where bottlenecks can dramatically increase the mixing time of quantum dynamical semigroups. In this work, we introduce a quantum analogue of the replica exchange method. We define a Lindbladian on a joint system of two replicas and prove that it can accelerate mixing for a class of Hamiltonians with local energy barriers. Our main result provides a rigorous lower bound on the spectral gap of the combined system's Lindbladian, which leads to an exponential improvement in spectral gap with respect to the barrier height. We showcase the applicability of our method with several examples, including the defected 1D Ising model at arbitrary constant temperature, and defected non-commuting local Hamiltonians at high temperature. Our work provides a rigorous acceleration mechanism for quantum Gibbs preparation.

*This work was supported in part by the Challenge Institute for Quantum Computation (CIQC) funded by National Science Foundation (NSF) through grant number OMA-2016245 (Z.C., J.B., L.L.), by the NSF under Grant No. CCF-2420130 (J.B.), and by the U.S. Department of Energy, Office of Science, Accelerated Research in Quantum Computing Centers, Quantum Utility through Advanced Computational Quantum Algorithms, grant no. DE-SC0025572 (L.L.). L.L. is a Simons Investigator in Mathematics.

Publication: https://arxiv.org/abs/2510.07291

Presenters

  • Zherui Chen

    • University of California, Berkeley

Authors

  • Zherui Chen

    • University of California, Berkeley
  • Joao Basso

    • University of California, Berkeley
  • Zhiyan Ding

    • University of Michigan
    • Department of Mathematics, University of Michigan, Ann Arbor
  • Lin Lin

    • University of California, Berkeley