Quantum information science is an interdisciplinary field closely related to computer science and physics. There are algorithmic tools from this field with computational applications in classical computer science and quantum physics. In this talk, I will introduce my work on developing these tools for solving problems in optimization, machine learning, and studying quantum systems. In particular, on the computer science side, I will discuss quantum speedups for optimization and machine learning via computational geometry problems. I will also describe quantum-inspired classical algorithms for solving machine learning problems. On the physics side, I will introduce quantum algorithms for simulating open quantum systems, as well as efficient constructions of pseudo-random quantum operators.
Chunhao Wang is a postdoctoral researcher in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in computer science from the University of Waterloo in 2018, where he was advised by Richard Cleve. His research aims to investigate the connections between quantum and classical algorithms and to find better quantum algorithmic tools related to physical systems.