Polaritonic Neuromorphic Computing Outperforms Linear Classifiers

Ballarini, Dario; Gianfrate, Antonio; Panico, Riccardo; Opala, Andrzej; Ghosh, Sanjib; Dominici, Lorenzo; Ardizzone, Vincenzo; Giorgi, Milena De; Lerario, Giovanni; Gigli, Giuseppe; Liew, Timothy Chi Hin; Matuszewski, Michał; Sanvitto, D.

doi:10.1021/acs.nanolett.0c00435

Cited by 89 publications

(85 citation statements)

References 60 publications

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“…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].…”

Section: Dn Basov Et Al: Polariton Panoramamentioning

confidence: 99%

Polariton panorama

et al. 2020

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In this brief review, we summarize and elaborate on some of the nomenclature of polaritonic phenomena and systems as they appear in the literature on quantum materials and quantum optics. Our summary includes at least 70 different types of polaritonic light–matter dressing effects. This summary also unravels a broad panorama of the physics and applications of polaritons. A constantly updated version of this review is available at https://infrared.cni.columbia.edu.

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Section: Dn Basov Et Al: Polariton Panoramamentioning

confidence: 99%

Polariton panorama

et al. 2020

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“…[ 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.…”

Section: Introductionmentioning

confidence: 99%

Quantum Neuromorphic Computing with Reservoir Computing Networks

et al. 2021

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Quantum reservoir networks combine the intelligence of neural networks with the potential of quantum computing in a single platform. This platform operates on the architecture of reservoir computing, which can function even with random connections between neural nodes. This is a major advantage for hardware implementation. Herein is described how reservoir computing is brought into the quantum domain to perform various tasks, including characterization of quantum states, quantum estimation, quantum state preparation, and quantum computing. It shows quantum enhancement in classical data processing, and creates the opportunity for quantum information processing within the robust paradigm of neural networks. It is friendly for implementation in a wide range of physical systems, including quantum dots, superconductors, trapped ions, cold atoms, and exciton‐polaritons.

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“…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].…”

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Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Ghosh

Krisnanda

Paterek

et al. 2021

Preprint

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Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. It induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, greatly reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.

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Polaritonic Neuromorphic Computing Outperforms Linear Classifiers

Cited by 89 publications

References 60 publications

Polariton panorama

Polariton panorama

Quantum Neuromorphic Computing with Reservoir Computing Networks

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

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