A Spontaneous Symmetry Breaking Machine -- A Theory for a Novel Type of Spontaneous Symmetry Breaking in a Unique Dissipative System and one Application
Toshiya Sato
TL;DR
This work investigates a photonic dissipative system where robust dissipative causality enables a novel spontaneous symmetry breaking (SSB) mechanism, both theoretically and experimentally. It introduces the full-dissipative connection system (FDCS) and shows how interconnecting multiple FDCS units via optical interference can produce complex, multi-element SSB that maps onto pseudo-spins and an Ising-like energy landscape. The authors propose the SSB machine (SSBM), a solver for combinatorial optimization problems, by encoding pseudo-spin interactions through optical interference and demonstrating, via numerical simulations on a MaxCut3 benchmark, that the system can reach stable Ising-like states with high reliability at low noise. This work highlights a duality-based perspective to tackle hard optimization problems and points toward scalable, causality-driven photonic platforms as alternative computational resources, with experimental demonstration of the SSBM as a future direction.
Abstract
We focus on an interesting dissipative system found in a photonics system. In this dissipative system, we theoretically identified that robust causality is generated and as a result, it becomes possible to produce behavior that can be understood as SSB, and, we experimentally demonstrated this finding. Furthermore, we theoretically demonstrated that by combining such dissipative systems as fundamental elements and establishing a certain relationship between them through optical interference, it is possible to create a unique system that generates complex SSB as a whole. This unique SSB can be understood as having a duality with the model of the creation of many-body-like system (MBLS), and by using the correspondence between the many-body-like system and the Ising model, it holds promise as an alternative computational resource for solving combinatorial optimization problems.
