Probing the Quantum-Classical Transition with Bayes-Enhanced Scanning Gate Microscopy

Abstract

The principal goal of this project was to devise techniques to obtain information from scanning gate microscopy images. For this, we have created numerical packages to compute non-equilibrium Greens functions (NEGF) and implemented a cellular neural network architecture to estimate the potential given a local density of states. We report the development of two successful approaches that can be used in quantum constrictions to estimate the alloy potential as well as the formation of charge puddles.

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Document Details

Document Type
Technical Report
Publication Date
Nov 22, 2022
Accession Number
AD1192114

Entities

People

  • Carlo R Da Cunha

Organizations

  • Federal University of Rio Grande do Sul

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Economic Systems
  • Energy Efficiency
  • Engineering
  • Greens Functions
  • Language
  • Learning
  • Machine Learning
  • Machines
  • Materials
  • Neural Networks
  • Parallel Computing
  • Parallel Processing
  • Quantum Dots
  • Quantum Properties
  • Scientific Research
  • Transitions

Readers

  • Computational Modeling and Simulation
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Quantum Computing
  • Quantum Science - Quantum Dots