QSE Seminar - Tongyang Li

Please join us for the QSE Center Quantum Seminar with Tongyang Li from the Center on Frontiers of Computing Studies, Peking University, who will speak Thursday July 3rd on "Quantum singular value transformation without block encodings: Near-optimal complexity with minimal ancilla". 
Location: BS 270

Pizzas will be available before the seminar at 12:00. All PhDs, postdocs, students, and PIs are welcome to join us.

TITLE: "Quantum singular value transformation without block encodings: Near-optimal complexity with minimal ancilla"

​​​​​​​ABSTRACT:
Quantum singular value transformation (QSVT) is a unifying framework that encapsulates most known quantum algorithms. However, existing implementations rely on block encoding, incurring an intrinsic O(log L) ancilla overhead when there are L terms. In this talk, I will introduce some methods for implementing QSVT without block encodings, based on our recent work [arXiv:2504.02385]. We propose algorithms that achieve near-optimal complexity using only a single ancilla qubit. One approach utilizes Trotter and Richardson extrapolation. Furthermore, we propose randomized QSVT algorithms for cases where only sampling access to the Hamiltonian terms is available. We also establish a fundamental lower bound of Ω(d^2) for any randomized method implementing polynomial transformations within this model.

BIO:
Tongyang Li is currently an assistant professor at the Center on Frontiers of Computing Studies, Peking University. Previously he was a postdoctoral associate at the Center for Theoretical Physics, Massachusetts Institute of Technology during 2020-2021. He received Ph.D. degree from the Department of Computer Science, University of Maryland in 2020. His research investigates interdisciplinary subjects among quantum computing, machine learning, and theoretical computer science, with the focus on designing quantum algorithms for optimization and machine learning. He is also interested in performing quantum algorithms on current noisy, intermediate-scale quantum devices (NISQ), as well as applying AI for better quantum algorithm design.