Southwest Quantum Information and Technology

Quantum Parallel Tempering

Presenting Author: Sam Slezak, University of New Mexico CQuIC
Contributing Author(s): Tameem Albash

Accurate and efficient sampling from thermal states of quantum systems is an essential ingredient for many computational tasks. On a quantum computer, the task of sampling from thermal states can be accomplished using the quantum Metropolis sampling algorithm, a quantum generalization of Markov chain Monte Carlo (MCMC) algorithms. In this work, we build on the quantum Metropolis sampling algorithm by introducing a quantum version of the parallel tempering algorithm, whereby many copies of the system called replicas are allowed to swap states and which classically is known to improve convergence times of MCMC algorithms. Our algorithm allows for the different replicas to not only have different temperatures but also to have different Hamiltonians, which allows for an interpolation between purely thermal tempering, where only the inverse-temperature is varied, and quantum tempering methods where specific terms in a Hamiltonian are varied.

(Session 5 : Thursday from 5:00 pm - 7:00 pm)