Automatic Differentiation for Inverse Problems with Applications in Quantum Transport
Ivan Williams, Eric Polizzi
TL;DR
This paper introduces a neural solver and differentiable simulation for inverse quantum transport problems, enabling the engineering of transmission properties and current-voltage characteristics in quantum systems.
Contribution
It presents a novel neural solver and differentiable simulation framework specifically designed for inverse quantum transport problems, advancing the ability to engineer quantum device properties.
Findings
Successful neural solver for quantum transport inverse problems
Differentiable simulation enables precise engineering of quantum device characteristics
Framework applicable to quantum device design and optimization
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.
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Taxonomy
TopicsNeural Networks and Applications