Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/224567
Title: TRANSFER LEARNING FOR NEURAL-NETWORK QUANTUM STATES
Authors: REMMY AUGUSTA MENZATA ZEN
ORCID iD:   orcid.org/0000-0002-7645-125X
Keywords: neural-network quantum state, transfer learning, quantum critical points, ground state, excited state
Issue Date: 30-Dec-2021
Citation: REMMY AUGUSTA MENZATA ZEN (2021-12-30). TRANSFER LEARNING FOR NEURAL-NETWORK QUANTUM STATES. ScholarBank@NUS Repository.
Abstract: Neural-network quantum state trains a neural network to be a surrogate of the wave function of a quantum many-body system. An important question in quantum many-body physics is the identification of the phases. Phase transitions occur at quantum critical points. Empirically, a quantum critical point can be identified by finding the inflection points of a physical quantity in the ground state. Our first and second contributions consist of several physics-informed transfer learning protocols to scale neural-network quantum states to larger sizes and explore the space of parameters for the effective and efficient identification of quantum critical points. We also devise transfer learning protocols to larger, deeper, and broader networks as the third contribution. Quantum critical points formally corresponding to closing spectral gap can be relevant to approximating the wave function of excited states. We devise and adopt four transfer learning protocols to achieve this purpose as our fourth contribution.
URI: https://scholarbank.nus.edu.sg/handle/10635/224567
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ZenRAM.pdf5.21 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

47
checked on Sep 22, 2022

Download(s)

12
checked on Sep 22, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.