Note:

1.The lecture on Sept. 24 is cancelled. The first lectures will be on Sept. 26, 13:30-15:05 & 15:20-16:55.

Venues:

13:30-15:05, Sept. 26: Shuangqing B626

15:20-16:55, Sept. 26: Shuangqing B627

2.The lecture on Oct. 1 will be moved to Sept. 29, B626. The lecture on Oct. 3 will be cancelled.

3. The lectures on Oct. 22 & Oct. 24 will be cancelled. And the last lectures will be on Dec. 3 & Dec. 5, 2024.

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

Registration: https://www.wjx.top/vm/rmgiPKu.aspx#

Short bio:

Jin-Peng Liu is an assistant professor at YMSC, Tsinghua University. 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).