Jiaqing Jiang
University of California, Berkeley
jiaqingjiang95@gmail.com
Bio
Jiaqing Jiang is a Quantum Postdoctoral Fellow at the Simons Institute for the Theory of Computing, hosted by Umesh Vazirani. She obtained her PhD from Caltech in 2025, under the supervision of Thomas Vidick, Urmila Mahadev, and John Preskill. She is broadly interested in quantum algorithm and quantum complexity, especially in understanding potential quantum advantage for solving quantum many-body systems, like estimating ground energy and preparing ground states / Gibbs states.
Areas of Research
- Theoretical Computer Science
Quantum Gibbs Sampling
Gibbs sampling is a crucial computational technique used in physics, statistics, and many other scientific fields. For classical Hamiltonians, the most commonly used Gibbs sampler is the Metropolis algorithm, known for having the Gibbs state as its unique fixed point. For quantum Hamiltonians, designing provably correct Gibbs samplers has been more challenging. [TOV+11] introduced a novel method that uses quantum phase estimation (QPE) and the Marriot-Watrous rewinding technique to mimic the classical Metropolis algorithm for quantum Hamiltonians. The analysis of their algorithm relies upon the use of a boosted and shift-invariant version of QPE which may not exist [CKBG23]. Recent efforts to design quantum Gibbs samplers take a very different approach and are based on simulating Davies generators [CKBG23,CKG23,RWW23,DLL24]. Currently, these are the only provably correct Gibbs samplers for quantum Hamiltonians. We revisit the inspiration for the Metropolis-style algorithm of [TOV+11] and incorporate weak measurement to design a conceptually simple and provably correct quantum Gibbs sampler, with the Gibbs state as its approximate unique fixed point. Our method uses a Boosted QPE which takes the median of multiple runs of QPE, but we do not require the shift-invariant property. In addition, we do not use the Marriott-Watrous rewinding technique which simplifies the algorithm significantly.