The problem of finding the smallest eigenvalue of a Hermitian matrix, known as the ground state energy in quantum physics, has broad applications. Recent years have witnessed significant algorithmic progresses including near-optimal asymptotic complexity, algorithms with a minimal number of required logical qubits, and even optimized preconstants. In this talk, I will first introduce basic quantum algorithm concepts for a non-expert audience and overview these advancements. I will then introduce a recent progress in leveraging ideas from open quantum systems to solve the eigenvalue problem, which allows us to start from a state with zero overlap with the target state.

Speaker Bio: Lin Lin is a Professor in the Department of Mathematics at UC Berkeley, and a Faculty Scientist in the Mathematics Group at Lawrence Berkeley National Laboratory. His research centers on solving quantum many-body problems by employing both classical and contemporary methods. These techniques prove valuable across various domains, including quantum chemistry, quantum physics, materials science, and quantum information theory. He has received the Sloan Research Fellowship (2015), the National Science Foundation CAREER award (2017), the Department of Energy Early Career award (2017), the (inaugural) SIAM Computational Science and Engineering (CSE) early career award (2017), the Presidential Early Career Awards for Scientists and Engineers (PECASE) (2019), the ACM Gordon Bell Prize (Team, 2020), and the Simons Investigator in Mathematics award (2021).

[Contact: E. Gull]