Jiaqi Leng

Jiaqi Leng is an assistant professor of computer science, based at the Virginia Tech Institute for Advanced Computing (IAC) in the greater Washington, D.C., area.

Leng comes to Virginia Tech from the University of California, Berkeley, where he served as a postdoctoral fellow at the Simons Institute for the Theory of Computing and the Mathematics Department. His research focuses on developing efficient quantum algorithms and supporting software for large-scale optimization, machine learning, and scientific computing. This work has been recognized in leading journals and peer-reviewed conference venues, including Proceedings of the National Academy of Sciences, Quantum Information Processing Conference, Conference on Neural Information Processing Systems, and the International Conference on Machine Learning.

Leng earned his Ph.D. in applied mathematics from the University of Maryland and his B.S. in mathematics from the University of Hong Kong.  

At Virginia Tech, Leng will develop quantum solutions that connect theoretical algorithmic advantages with practical, hardware-efficient implementations. His work includes Quantum Hamiltonian Descent (QHD), a quantum analogue of gradient descent that uses quantum tunneling to achieve exponential speedups in complex, non-convex optimization problems. He is also the creator of QHDOPT, an open-source software platform that brings QHD to both gate-based and analog quantum hardware. Leng has additionally developed differentiable hybrid quantum-classical frameworks for optimal control, with applications to large-scale AI and robotics problems.