Author(s): Chesson Sipling, Yuan-Hang Zhang, and Massimiliano Di Ventra Physics-based memcomputing is of interest for solving hard combinatorial-optimization problems in computer science, engineering, and physics by embedding a problem directly into the dynamics of a nonlinear system with memory. Few studies, however, have addressed how the phase-space structure controls performance and scalability. This work uses systematic phase-space engineering and simulations of memcomputing machines to identify the dynamical mechanisms that enable efficient solution-finding. Its insights into how memory-driven collective behavior influences phase-space geometry could help in designing more reliable and scalable physics-inspired devices for hard computational problems. [Phys. Rev. Applied 25, 014048] …
Author(s): Chesson Sipling, Yuan-Hang Zhang, and Massimiliano Di Ventra Physics-based memcomputing is of interest for solving hard combinatorial-optimization problems in computer science, engineering, and physics by embedding a problem directly into the dynamics of a nonlinear system with memory. Few studies, however, have addressed how the phase-space structure controls performance and scalability. This work uses systematic phase-space engineering and simulations of memcomputing machines to identify the dynamical mechanisms that enable efficient solution-finding. Its insights into how memory-driven collective behavior influences phase-space geometry could help in designing more reliable and scalable physics-inspired devices for hard computational problems. [Phys. Rev. Applied 25, 014048] Published Wed Jan 21, 2026