Jiaqi Leng

Jiaqi Leng is a Simons Quantum Postdoctoral Fellow in the Department of Mathematics at the University of California, Berkeley. Their research focuses on quantum algorithms for optimization and scientific computing. Leng's work includes developing quantum Hamiltonian descent methods and exploring quantum-classical performance separations in nonconvex optimization. They have contributed to preprints on quantum central path algorithms, hardware-efficient Hamiltonian embeddings, and differentiable analog quantum computing. Leng's publications appear in venues such as Advances in Neural Information Processing Systems and Quantum. Their contact information is jiaqil@berkeley.edu, and their personal webpage is available at https://math.berkeley.edu/people/jiaqi-leng.

Jiaqi Leng is a researcher at the University of California, Berkeley, focusing on Quantum Computation, Optimization, and Scientific Computing. Their work explores quantum algorithms for optimization problems, including quantum Hamiltonian descent and quantum central path algorithms for linear optimization. They have developed software tools like Qhdopt for nonlinear optimization using quantum Hamiltonian descent. Research also includes differentiable analog quantum computing for control and optimization, as well as quantum-inspired methods for mixed-integer quadratic programming. Their publications address quantum speedup in optimization, quantum simulation of dynamics, and quantum Gibbs sampling. Leng's research bridges quantum theory with computational methods to enhance efficiency in scientific computing and optimization tasks.