Automatic Differentiation for Inverse Problems with Applications in Quantum Transport

07/18/2023
by   Ivan Williams, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/22/2021

DQC: a Python program package for Differentiable Quantum Chemistry

Automatic differentiation represents a paradigm shift in scientific prog...
research
07/23/2023

JAX FDM: A differentiable solver for inverse form-finding

We introduce JAX FDM, a differentiable solver to design mechanically eff...
research
09/18/2023

A Quantum Optimization Case Study for a Transport Robot Scheduling Problem

We present a comprehensive case study comparing the performance of D-Wav...
research
12/18/2019

Optimizing the Data Movement in Quantum Transport Simulations via Data-Centric Parallel Programming

Designing efficient cooling systems for integrated circuits (ICs) relies...
research
12/18/2019

A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations

The computational efficiency of a state of the art ab initio quantum tra...
research
12/13/2022

Generating extreme quantum scattering in graphene with machine learning

Graphene quantum dots provide a platform for manipulating electron behav...
research
11/08/2022

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the develop...

Please sign up or login with your details

Forgot password? Click here to reset