Deep-learning-empowered programmable topolectrical circuits

Topolectrical circuits provide a versatile platform for exploring modern physical models, yet existing approaches lack full programmability and effective mechanisms for inverse state design. Here, we present a deep-learning-empowered programmable topolectrical circuit platform for physical modeling and analysis. Our system integrating fully independent, continuous tuning of on-site and off-site Hamiltonian terms, physics-graph-informed inverse state design, and immediate hardware verification, thereby bridges theoretical modeling and practical realization. Through flexible control and adiabatic path engineering, we experimentally observe boundary states in higher-order topological systems without global symmetry, the associated adiabatic phase transitions, and flat-band characteristics corresponding to Landau levels. Incorporating a physics-graph-informed generative model, we achieve arbitrary, position-controllable Anderson localization surpassing conventional random approaches. Leveraging this capability, we demonstrate physics-mechanism-driven probabilistic information encryption and product anti-counterfeiting. Our work establishes a paradigm where deep learning and programmable hardware synergistically enable on-demand inverse design, bridging physics and information technologies.