Deep-Learning-Empowered Programmable Topolectrical Circuits

TL;DR

This paper introduces a deep learning-enabled programmable topolectrical circuits platform that allows precise control and analysis of complex physical phenomena, including topological states, phase transitions, and cryptographic applications, bridging theory and practical hardware implementation.

Contribution

The work presents a novel integrated system combining deep learning, physics-informed design, and hardware verification for programmable topolectrical circuits, enabling advanced physical modeling and secure information encryption.

Findings

Experimental observation of boundary states without global symmetry

Realization of adiabatic phase transitions and flat band characteristics

Implementation of secure hash-based probabilistic encryption

Abstract

Topolectrical circuits provide a versatile platform for exploring and simulating modern physical models. However, existing approaches suffer from incomplete programmability and ineffective feature prediction and control mechanisms, hindering the investigation of physical phenomena on an integrated platform and limiting their translation into practical applications. Here, we present a deep learning empowered programmable topolectrical circuits (DLPTCs) platform for physical modeling and analysis. By integrating fully independent, continuous tuning of both on site and off site terms of the lattice Hamiltonian, physics graph informed inverse state design, and immediate hardware verification, our system bridges the gap between theoretical modeling and practical realization. Through flexible control and adiabatic path engineering, we experimentally observe the boundary states without global symmetry in higher order topological systems, their adiabatic phase transitions, and the flat band like characteristic corresponding to Landau levels in the circuit. Incorporating a physics graph informed mechanism with a generative AI model for physics exploration, we realize arbitrary, position controllable on board Anderson localization, surpassing conventional random localization. Utilizing this unique capability with high fidelity hardware implementation, we further demonstrate a compelling cryptographic application: hash based probabilistic information encryption by leveraging Anderson localization with extensive disorder configurations, enabling secure delivery of full ASCII messages.