Jiaqi (Jimmy) Leng

I am a Simons Quantum Postdoctoral Fellow at Simons Institute for the Theory of Computing at UC Berkeley, hosted by Umesh Vazirani and Lin Lin. I got my Ph.D. from the University of Maryland in 2024, where I was fortunate to be advised by Xiaodi Wu. My name in Chinese: 冷佳奇.
My research focuses on the interplay between machine learning and quantum computation. I design quantum algorithms inspired by core principles in modern ML, such as optimization, sampling, and differentiable programming. To make these algorithms practical, I build scalable and automated toolchains for their simulation and deployment on current and near-term quantum computers. Recently, I am also interested in quantum-inspired methods amenable to large-scale acceleration on today’s classical processors.
news
Sep 21, 2025 | Our submission “Quantum-Inspired Hamiltonian Descent for Mixed-Integer Quadratic Programming” has been accepted by the NeurIPS 2025 Workshop ScaleOPT: GPU-Accelerated and Scalable Optimization as a poster. |
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Sep 03, 2025 | My collaborators (Yuxiang Peng, Lei Fan, and Xiaodi Wu) and I organize a tutorial titled “Step-by-Step Guide to Solving Nonlinear Optimization with Quantum Computers” at the IEEE Quantum Week (QCE25). Check it out if you are attending! |
Jul 24, 2025 | I chair a session “Quantum Methods for Optimization and Sampling” at the International Conference on Continuous Optimization (ICCOPT 2025), in which I also give a talk on a new quantum algorithm for Gibbs sampling with continuous potentials. |