Quantum Gibbs Sampling

Citation

Chen, Chi-Fang (2025) Quantum Gibbs Sampling. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/dy0f-3216. https://resolver.caltech.edu/CaltechTHESIS:08152024-132609600

Abstract

Markov Chain Monte Carlo algorithms are indispensable in classical thermodynamic simulation, perhaps due to their mathematical simplicity, algorithmic efficiency, and physical origin. In particular, Glauber dynamics is a detailed-balanced continuous-time Markov chain that fixes the Gibbs distribution and also serves as a mathematically succinct model of classical thermalization. In this thesis, we proposed a quantum computation analog of Glauber dynamics that is exactly detailed balanced yet algorithmic efficient, inherits the locality of the target Hamiltonian, and resembles Davies'-like generators physically derived from a weak system-bath coupling. We hope our proposal will serve as a quantum algorithm for quantum thermodynamic simulation and a model of open system thermalization where a suitable construction has been lacking for noncommuting Hamiltonians.

Thesis Files