Road to conquer the hardness - solving hard computational problems with generic tensor networks

Time:2022-08-23 13:30-15:00 Tue

Venue:Zoom 427 154 2002(PW: BIMSA)


Speaker: Jinguo Liu Harvard University and Hong Kong University of Science and Technology


I will introduce a tensor network-based method to compute the solution space properties of a broad class of combinatorial optimization problems (e.g. spin glasses and hard core lattice gases). These properties include finding one of the optimum solutions, counting the number of solutions of a given size, and enumeration and sampling of solutions of a given size. Using the hard core lattice gas as an example, I will demonstrate how the solution space properties can deepen our understanding, and help design better quantum algorithms.

DATEAugust 22, 2022
Related News
    • 0

      Optimization Problems and Approaches in Computational Materials Science

      AbstractAccelerated by the ever-growing power of computers, computational materials science has underpinned materials modeling and simulation. Many ingredients in this field, from both electronic structure and atomistic levels, can be (re)formulated into optimization problems. Numerous optimization approaches have been constantly emerging, unleashing their exceptional efficiency, robustness, an...

    • 1

      Conductivity Imaging using Deep Neural Networks

      Speaker Dr. Bangti Jin received his PhD degree in applied mathematics from the Chinese University of Hong Kong, Hong Kong, in 2008. Currently he is a professor of mathematics at Department of Mathematics, The Chinese University of Hong Kong. Previously he was a lecturer, reader and professor of inverse problems at Department of Computer Science, University College London (2014-2022), an assista...