[Joint CQSE & NCTS Seminar] Provably efficient machine learning for quantum many-body problems

  • Event Date: 2022-09-30
  • Quantum Information and Quantum Computing
  • Speaker: Mr. Hsin-Yuan Huang (Institute for Quantum Information and Matter, Caltech)  /  Host: Prof. Hsi-Sheng Goan (NTU)
    Place: Rm. 104, Chin-Pao Yang Lecture Hall, CCMS & New Physics Building, NTU

Title: [Joint CQSE & NCTS Seminar] Provably efficient machine learning for quantum many-body problems
Speaker: Mr. Hsin-Yuan Huang (Institute for Quantum Information and Matter, Caltech)
Time: Sep. 30, 2022, 14:30-15:30
Place: Rm. 104, Chin-Pao Yang Lecture Hall, CCMS & New Physics Building, NTU
Online: https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my/j.php?MTID=mfb9740abc62590ec301de893cd73304b

Abstract:
Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the advantages
of ML over more traditional methods have not been firmly established. In this work, we prove
that classical ML algorithms can efficiently predict ground state properties of gapped
Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other
Hamiltonians in the same quantum phase of matter. In contrast, under widely accepted
complexity theory assumptions, classical algorithms that do not learn from data cannot achieve
the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide
range of quantum phases of matter. Our arguments are based on the concept of a classical
shadow, a succinct classical description of a many-body quantum state that can be constructed
in feasible quantum experiments and be used to predict many properties of the state. Extensive
numerical experiments corroborate our theoretical results in a variety of scenarios, including
Rydberg atom systems, 2D random Heisenberg models, symmetry-protected topological phases,
and topologically ordered phases.

Biography:
Education:
Ph.D., California Institute of Technology
Oct. 2018 - Now
Advised by John Preskill (Physics) and Thomas Vidick (CS).

B.S., National Taiwan University
Sep. 2014 - Jun. 2018
Studied in Computer Science (major) and Physics (minor). GPA: 4.30/4.30, Rank:
1/120. Member of the Machine Learning and Data Mining Group; Advisor: Chih-
Jen Lin

Research Experience:
Research Assistant, Institute for Quantum Information and Matter, Caltech
Oct. 2018 - Now

Research Intern, AWS Center for Quantum Computing, Mentor: Steven T. Flammia
Jun. 2021 - Sep. 2021

Research Intern, Google AI Quantum, Mentor: Jarrod R. McClean
Jun. 2020 - Oct. 2020

Visitor, Centre for Quantum Technologies, Host: Patrick Rebentrost
Jul. 2019 - Aug. 2019

Research Intern, Allen Institute for Artificial Intelligence, Mentor: Wen-tau Yih Jun.
2018 - Sep. 2018

Research Intern, Microsoft Research, Redmond, USA, Mentor: Chenguang Zhu
Jun. 2017 - Sep. 2017

Research Assistant, Dept. of Computer Science, NTU, PI: Chih-Jen Lin
Sep. 2014 - Jun. 2018

Research Assistant, Dept. of Life Science, NTU, PI: Hsueh-Fen Juan
May 2013 - Aug. 2014

Research Assistant, Institute of Earth Sciences, Academia Sinica, PI: Fong Chao
Mar. 2012 - Mar. 2013