Nanoscale photonic network for solution searching and decision making problems

TL;DR

This paper proposes a nanoscale photonic network utilizing quantum dot energy transfers for solving complex computational problems like CSP, SAT, and decision making, demonstrating potential for low-energy, physics-inspired computing.

Contribution

It introduces a novel quantum nanostructure network approach for solution searching and decision making, leveraging optical near-field interactions at sub-wavelength scales.

Findings

Networks can solve CSP, SAT, and decision problems.

Optical energy transfer patterns evolve to find solutions.

Approach offers low energy consumption for computation.

Abstract

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-field interactions occurring at scales far below the wavelength of light could be utilized for solving a constraint satisfaction problem (CSP), the satisfiability problem (SAT), and a decision making problem. The optical energy transfer from smaller quantum dots to larger ones, which is a quantum stochastic process, depends on the existence of resonant energy levels between the quantum dots or a state-filling effect occurring at the larger quantum dots. Such a spatiotemporal mechanism yields different evolutions of energy transfer patterns in multi-quantum-dot systems. We numerically demonstrate that networks of optical energy transfers can be used for solution searching and decision making. We consider that such an approach paves the way to a novel physical informatics in which both coherent and dissipative processes are exploited, with low energy consumption.