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Automatic Differentiation for Inverse Problems with Applications in Quantum Transport

Machine Learning 2023-07-19 v1 Computational Engineering, Finance, and Science Computational Physics
Authors: Ivan Williams , Eric Polizzi
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Abstract

A neural solver and differentiable simulation of the quantum transmitting boundary model is presented for the inverse quantum transport problem. The neural solver is used to engineer continuous transmission properties and the differentiable simulation is used to engineer current-voltage characteristics.

Keywords

quantum computing

Cite

@article{arxiv.2307.09311,
  title  = {Automatic Differentiation for Inverse Problems with Applications in Quantum Transport},
  author = {Ivan Williams and Eric Polizzi},
  journal= {arXiv preprint arXiv:2307.09311},
  year   = {2023}
}

Comments

7 pages, 5 figures

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