Automatic Differentiation for Inverse Problems with Applications in Quantum Transport

This work addresses quantum transport engineering, likely for physicists or engineers, but appears incremental as it applies existing neural and differentiable methods to a specific domain.

The authors tackled the inverse quantum transport problem by developing a neural solver and differentiable simulation for the quantum transmitting boundary model, enabling engineering of continuous transmission properties and current-voltage characteristics.