Abstract:
With recent experimental progress in quantum computing technology, unprecedented programmability has been achieved in various platforms from superconducting qubits and trapped ions to cold atoms and Rydberg tweezer arrays. This gives new opportunities for Hamiltonian quantum simulations. In this talk I will describe algorithmic approach for Hamiltonian quantum simulations, which may be used to solve difficult computation problems. For example, binary optimization problems and their related graph partitions can be encoded by all-to-all coupled spin glass models, which we propose to simulate by a completely local spin model on a cubic lattice with a quantum wiring protocol. Our wiring protocol has recently been realized in a Rydberg experiment. I will also present an algorithmic approach to simulate quantum thermal ensemble by “quantizing” the kernel function expansion algorithm. We developed QKFE (quantum kernel function expansion) and THEI (thermal ensemble iteration) algorithms, by which the thermodynamical partition function and the thermal entropy can be computed at a polynomial cost.
Biography:
Xiaopeng Li is the Young Xie Xide Chair Professor in the Department of Physics at Fudan University. He received his BSc from the University of Science and Technology of China (2008) and PhD from the University of Pittsburgh (2013). After postdoctoral research at the University of Maryland (2013–2016), he joined Fudan as a Young Researcher in late 2016. His recognitions include selection to the Overseas High-Level Talent Program (2017), Forbes China U30 Science & Technology Elite List (2017), promotion to Full Professor (2019), appointment as Distinguished Scientist at Shanghai Qizhi Institute (2020), and receipt of the Shanghai Youth May 4th Medal (2024). His research focuses on quantum simulation and quantum computing, with current emphasis on developing fault-tolerant quantum computers using neutral atoms.
Date & Time: 10:30-12:00 April 9 (Wednesday), 2025
Venue: TDLI Meeting Room N400
Tencent Meeting link:
https://meeting.tencent.com/dm/b4o3xoXfJwpg Meeting ID: 317627086, no password