Quantum computational advantage with constant-temperature Gibbs sampling

TL;DR

This paper demonstrates that sampling from quantum Gibbs states at constant temperature can achieve quantum computational advantage by designing rapidly thermalizing Hamiltonians and proving classical intractability of the sampling task.

Contribution

It introduces a family of local Hamiltonians that thermalize quickly and proves classical hardness of sampling from their Gibbs states, establishing quantum advantage in realistic thermal conditions.

Findings

Quantum Gibbs sampling can demonstrate quantum advantage.

Designed Hamiltonians rapidly reach Gibbs states under physical thermalization.

Classical algorithms cannot efficiently sample from these quantum distributions.

Abstract

A quantum system coupled to a bath at some fixed, finite temperature converges to its Gibbs state. This thermalization process defines a natural, physically-motivated model of quantum computation. However, whether quantum computational advantage can be achieved within this realistic physical setup has remained open, due to the challenge of finding systems that thermalize quickly, but are classically intractable. Here we consider sampling from the measurement outcome distribution of quantum Gibbs states at constant temperatures, and prove that this task demonstrates quantum computational advantage. We design a family of commuting local Hamiltonians (parent Hamiltonians of shallow quantum circuits) and prove that they rapidly converge to their Gibbs states under the standard physical model of thermalization (as a continuous-time quantum Markov chain). On the other hand, we show that no polynomial time classical algorithm can sample from the measurement outcome distribution by reducing to the classical hardness of sampling from noiseless shallow quantum circuits. The key step in the reduction is constructing a fault-tolerance scheme for shallow IQP circuits against input noise.