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Learning in the Quantum Universe

Citation

Huang, Hsin-Yuan (2024) Learning in the Quantum Universe. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/fgpv-3112. https://resolver.caltech.edu/CaltechTHESIS:05032024-044352582

Abstract

In this thesis, I will present our progress in building a rigorous theory to understand how scientists, machines, and future quantum computers could learn models of our quantum universe. The thesis begins with an experimentally feasible procedure for converting a quantum many-body system into a succinct classical description of the system, its classical shadow. Classical shadows can be applied to efficiently predict many properties of interest, including expectation values of local observables and few-body correlation functions. I will then build on the classical shadow formalism to answer two fundamental questions at the intersection of machine learning and quantum physics: Can classical machines learn to solve challenging problems in quantum physics? And can quantum machines learn exponentially faster and predict more accurately than classical machines? The thesis answers both questions positively through mathematical analysis and experimental demonstrations.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Quantum information; Learning theory; Quantum computing; Quantum many-body physics
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computing and Mathematical Sciences
Awards:Google PhD Fellowship Boeing Quantum Creators Prize MediaTek Research Young Scholarship J. Yang Scholarship Kortschak Scholars Fellowship Taiwan Government Scholarship to Study Abroad
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Preskill, John P. (advisor)
  • Vidick, Thomas George (co-advisor)
Group:Institute for Quantum Information and Matter
Thesis Committee:
  • Brandao, Fernando (chair)
  • Tropp, Joel A.
  • Endres, Manuel A.
  • Preskill, John P.
  • Vidick, Thomas Georges
Defense Date:17 August 2023
Record Number:CaltechTHESIS:05032024-044352582
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:05032024-044352582
DOI:10.7907/fgpv-3112
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/FOCS52979.2021.00063DOIArticle adapted for Ch. 2 and Ch. 3
https://doi.org/10.1038/s41467-021-22539-9DOIArticle adapted for Ch. 3 and Ch. 8
https://doi.org/10.1103/PhysRevLett.127.030503DOIArticle adapted for Ch. 4
https://doi.org/10.1038/s41567-020-0932-7DOIArticle adapted for Ch. 4
https://doi.org/10.1126/science.abk3333DOIArticle adapted for Ch. 5
https://doi.org/10.1038/s41467-024-45014-7DOIArticle adapted for Ch. 5
https://doi.org/10.1103/PRXQuantum.4.040337DOIArticle adapted for Ch. 6
https://doi.org/10.1103/PhysRevLett.126.190505DOIArticle adapted for Ch. 7
https://doi.org/10.1126/science.abn7293DOIArticle adapted for Ch. 9
ORCID:
AuthorORCID
Huang, Hsin-Yuan0000-0001-5317-2613
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16367
Collection:CaltechTHESIS
Deposited By: Robert Huang
Deposited On:14 May 2024 18:18
Last Modified:28 May 2024 16:44

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