Induced Homomorphism Kirchhoffs Law in Photonics

TL;DR

This paper introduces a photonic platform that mimics Kirchhoff's law, enabling fast, chip-scale solutions to partial differential equations with high accuracy, leveraging the wave nature of light for analog computing.

Contribution

The authors develop a novel photonic system that induces a homomorphism to Kirchhoff's law, allowing for efficient PDE solving on integrated photonic chips.

Findings

Achieved over 95% accuracy compared to commercial PDE solvers.

Demonstrated one-shot discrete solutions of Laplace's equation.

Enabled fast, reprogrammable PDE solving on photonic hardware.

Abstract

When solving, modelling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be used for representing mechanical and acoustic systems, thermal and heat-diffusion problems and in general partial differential equations. Integrated photonic platforms hold the prospect to perform signal processing and analog computing inherently, by mapping into hardware specific operations which relies on the wave-nature of their signals, without trusting on logic gates and digital states like electronics. Although, the distributed nature of photonic platforms leads to the absence of an equivalent approximation to Kirchhoffs law, the main principle used for representing physical systems using circuits. Here we argue that in absence of a straightforward parallelism and homomorphism can be induced. We introduce a photonic platform capable of mimicking Kirchhoffs law in photonics and used as node of a finite difference mesh for solving partial differential equation using monochromatic light in the telecommunication wavelength. We experimentally demonstrate generating in one-shot discrete solutions of a Laplace partial differential equation, with an accuracy above 95% relative to commercial solvers, for an arbitrary set of boundary conditions. Our photonic engine can provide a route to achieve chip-scale, fast (10s of ps), and integrable reprogrammable accelerators for the next generation hybrid high performance computing.