Entropy Computing, A Paradigm for Optimization in Open Photonic Systems

TL;DR

This paper introduces the entropy computing paradigm in photonics, demonstrating a hybrid optical-electronic system capable of solving complex non-convex and combinatorial optimization problems using photonic modes and feedback.

Contribution

It presents the first experimental implementation of entropy computing in an optical setting, encoding Hamiltonians in a hybrid system for solving NP-hard optimization problems.

Findings

Successfully built a hybrid photonic-electronic entropy computing system

Demonstrated the system's ability to solve non-convex and combinatorial optimization problems

Showed potential for scalability and versatility in tackling NP-hard problems

Abstract

Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimisation paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems.