A Data-driven Approach to Correlated Quantum Many-Body Problems

Abstract

The final report of grant FA9550-18-1-0515 details the developments and successes made in the computational challenge posed by quantum many-body problems throughout chemistry, materials science and condensed matter fields of research, as part of the AFOSR computational mathematics programme. The work focused on the development of the Gaussian Process State as a novel, data-driven approach to describing quantum many-body states, their optimization and physical understanding. It has brought together the fields of machine-learning, electronic structure and function optimization in a novel approach to enable beyond state-of-the-art calculations on a number of key correlated quantum systems.

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Document Details

Document Type
Technical Report
Publication Date
Nov 25, 2022
Accession Number
AD1190029

Entities

People

  • George H Booth

Organizations

  • King's College London

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Research Laboratories
  • Availability
  • Bayesian Inference
  • Bayesian Networks
  • Classification
  • Computational Science
  • Contracts
  • Data Science
  • Estimators
  • Gaussian Processes
  • Information Science
  • Machine Learning
  • Materials
  • Materials Science
  • Mathematics
  • Monitoring
  • Organizational Structure
  • Quantum States
  • Scientific Research
  • Security
  • Statistical Algorithms
  • Subatomic Particles
  • Virginia

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Quantum Chemistry
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Microelectronics
  • Quantum Computing