Polaritonic Neuromorphic Computing Outperforms Linear Classifiers

Abstract

Nanyang Technological University 637371, SingaporeMachine learning software applications are nowadays ubiquitous in many fields of science and society for their outstanding capability of solving computationally vast problems like the recognition of patterns and regularities in big datasets. One of the main goals of research is the realization of a physical neural network able to perform data processing in a much faster and energyefficient way than the state-of-the-art technology. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing using fast optical nonlinearities and with lower error rate than any previous hardware implementation. We demonstrate that our neural network significantly increases the recognition efficiency compared to the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in reservoir computing architectures.

Polariton panorama

“…The use of exciton polaritons as building blocks for future information processing such as spin switches [409], spin memory [410], transistors [411], logic gates [412], resonant tunneling diodes [413], routers [414], and lasers [415] has recently been demonstrated. The first polaritonic systems are also emerging and include QSs and networks for neuromorphic computers [416]. TMDC material WSe 2 integrated into microcavity devices acts as efficient light emitting device [417].…”

Quantum Neuromorphic Computing with Reservoir Computing Networks

“…[ 25,26 ] Reservoir networks have been physically realized with a variety of hardware approaches, [ 27,28 ] including photonic arrays on silicon chips, [ 22 ] nonlinear optical elements coupled to delay lines, [ 23,24,29–31 ] magnetic tunnel junctions, [ 32 ] memristor arrays, [ 33–35 ] and exciton‐polariton lattices. [ 36,37 ] Given that reservoir networks have been an accessible design for different hardware, they seem a natural candidate for building quantum neural networks, where the nodes of classical reservoir networks are replaced with quantum entities. Here we review recent progress in the development of quantum reservoir networks.…”

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

“…Here, the fixed random network is known as the "reservoir". As it is easier to engineer a fixed and random network than a well controlled one, reservoir computing has been successfully implemented in a variety of physical systems [20][21][22][23]. Recently, the reservoir computing concept was brought to the quantum domain [24], using networks of quantum nodes [25,26] and the performance of specific non-classical tasks [27] including quantum state preparation [28] and tomography [29].…”