Optical computing

Abstract

We consider optical computers that encode data using images and compute by transforming such images. We give an overview of a number of such optical computing architectures, including descriptions of the type of hardware commonly used in optical computing, as well as some of the computational efficiencies of optical devices. We go on to discuss optical computing from the point of view of computational complexity theory, with the aim of putting some old, and some very recent, results in context. Finally, we focus on a particular optical model of computation called the continuous space machine. We describe some results for this model including characterisations in terms of well-known complexity classes.

Optical computing with soliton trains in Bose–Einstein condensates

“…Solitons could be observed in a wide range of different physical situations among which optical solitons [36] are widely used in today's technology [37]. For several decades light waves have been utilized for a wide range of applications such as in nonlinear fibre optic communication [37][38][39][40] while research on new technologies is thriving in particular on elementary circuit components such as diodes [41] or transistors [42] utilizing light or realized in polariton condensates [43,44] and conceptually on optical computing schemes [45].…”

Optical integration of a real-valued function by measurement of a Stokes parameter

“…Key features of optical information processing is the possibility of parallelization using optical components such as lenses and holograms [8], and the ability to perform the optical Fourier transform with diffraction or linear optical systems. Compared to digital electronic computing, optical computing presents a distinct paradigm for computation, which poses interesting fundamental questions of computational complexity [9], and provides alternative methods for sophisticated computational problems. In this regard, optical implementations have been presented for bounded NP-complete problems such as the traveling salesman [10][11][12][13][14], and artificial neural networks [15,16].…”

Boolean logic gate design principles in unconventional computers: an NMR case study

“…There is much work on in materio computing: exploiting unconventional material substrates and their dynamical properties to perform computation, and examining their computational properties and abilities. One of the more advanced is optical computing [33,34]. Other, more exotic, substrates include nuclear spins in NMR experiments [16,17,24,6], liquid crystals [12,13,14], conductive media [21,22], reaction-diffusion chemical systems [19,23,27,32], DNA [3,4], and even slime moulds [2,31].…”