USING ENTANGLEMENT ASPECTS, FOR EXAMPLE, BLOCK CORRELATION MATRICES, AND DEEP LEARNING ONE CAN DETERMINE THE TOPOLOGICAL PROPERTIES SUCH AS TOPOLOGICAL PHASE TRANSITIONS

Abstract

In this proposal, we study the topological matters in condensed matter by using quantum information and deep learning. In order to understand the amount of information carried in the entanglement-related quantities, here we study topological phase transitions of the model with emphasis of using the deep learning approach. Our goal is to investigate which information quantities are sufficient for distinguish different phases in an accurate way.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 11, 2021
Source ID
FA23862014049

Entities

People

  • Ming-chiang Chung

Organizations

  • Air Force Office of Scientific Research
  • National Chung Hsing University
  • United States Air Force

Tags

Readers

  • Neural Network Machine Learning.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Quantum Computing