Abstract

Sampling from a given distribution is a fundamental computational problem and has broad applications in statistics, machine learning, physics, etc. We systematically investigate the quantum speedup of Monte Carlo methods, quantum mean estimation, and fast-forwarding of reversible Markov chains. We develop quantum algorithms for sampling log-concave distributions (with density e^{-f(x)} for convex f(x)) and for estimating their normalizing constants, achieving polynomial speedups in query complexity over the best-known classical algorithms. This is a joint work with Andrew M. Childs, Tongyang Li, Chunhao Wang, and Ruizhe Zhang.

Reference:

[1] Quantum algorithms for sampling log-concave distributions and estimating normalizing constants.

https://arxiv.org/abs/2210.06539

Speaker

Jin-Peng Liu is a Simons Quantum Postdoctoral Fellow at Simons Institute, UC Berkeley in 2022-2023 (hosted by Umesh Vazirani and Lin Lin). He will be a Postdoctoral Associate at the Center for Theoretical Physics, MIT in 2023-2024 (hosted by Aram Harrow). He received a Ph.D. in applied mathematics at University of Maryland in 2022 spring (advised by Andrew Childs). He received a B.S. in math at Beihang University and Chinese Academy of Sciences Hua Loo Keng Class (supervised by Ya-xiang Yuan and Cong Sun).

Jin-Peng is serving as an editor of the journal Quantum from 2023. He received the NSF QISE-NET Triplet Award in 2021. His research focuses on Quantum for Science. He attempts to develop, analyze, and optimize provably efficient quantum algorithms for computational challenges in natural and data sciences, including quantum simulations, quantum ODE/PDE solvers, q-sampling, and quantum machine learning.