Speaker：Jin-Peng Liu 刘锦鹏
Jin-Peng Liu is an Assistant Professor at YMSC. He was a Simons quantum postdoctoral fellow at MIT and Berkeley from 2022 to 2024. He received his Ph.D. from the University of Maryland in 2022. His research focuses on Quantum for Science and AI+QS. He has published papers in PNAS, Nat. Commun., PRL, CMP, JCP, Quantum, and NeurIPS, QIP, TQC. His research has been reported by Quanta, SIAM News, and MATH+. He has won the ICCM Best Thesis Award (Gold Prize), NSF Robust Quantum Simulation Seed Grant (CO-PI), NSF QISE-NET Triplet Award, and James C. Alexander Prize. He is serving as an editor of Quantum (JCR Q1, IF 6.4).
Time：
Tues. & Thur.,13:30-15:05,
Sept. 24-Nov. 28, 2024
Venue：
B626, Shuangqing Complex Building A
Description:
Quantum computers have the potential to revolutionize how we think about computing. Central to quantum computation are quantum algorithms, which often differ considerably from classical algorithms. This is an advanced course that introduces quantum algorithms essential for scientific computation and artificial intelligence. Topics include Hamiltonian simulation, phase estimation, amplitude estimation, block encoding, quantum singular value transformation, and their applications in tasks like solving linear systems, eigenvalue problems, differential equations, optimization, and machine learning problems. The focus is on algorithmic components, design, and analysis. The quantum algorithms discussed are largely independent of the specific physical hardware on which they're implemented. Upon completing the course, students will have a solid understanding of the primary quantum algorithmic techniques for scientific computation and artificial intelligence, and will be prepared to engage with technical discussions and design novel quantum algorithms in their research.
Prerequisite:
Linear Algebra; Quantum Machanics or Quantum Information
Reference:
Lin Lin. Lecture Notes on Quantum algorithms for scientific computation
Andrew Childs. Lecture Notes on Quantum Algorithms
Target Audience:
Undergraduate students, Graduate students
Teaching Language: English
