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Neuromrophic Quantum Computing

Descrizione del progetto

DEENESFRITPL

Le reti neurali potrebbero presto essere eseguite su hardware quantistico

Le reti neurali artificiali, che simulano il modo in cui il cervello umano analizza ed elabora le informazioni, sono utilizzate per modellare schemi complessi e problemi di previsione. Questo approccio in genere implica la realizzazione di software piuttosto che la creazione di hardware che imiti i neuroni. Il progetto Quromorphic, finanziato dall’UE, prevede di implementare il calcolo neuromorfico a livello hardware. Il progetto si propone di costruire il primo computer di rete neurale dedicato funzionante in base ai principi della meccanica quantistica. Il computer sarà realizzato in hardware costituito da circuiti elettrici superconduttori. L’hardware quantistico neuromorfico potrebbe forse superare le classiche architetture di von Neumann, in quanto può essere addestrato su più batch di dati del mondo reale in parallelo.

Obiettivo

The Quromorphic project will introduce human brain inspired hardware with quantum functionalities: It will build superconducting quantum neural networks to develop dedicated, neuromorphic quantum machine learning hardware, which can, in its next generation, outperform classical von Neumann architectures. This breakthrough will combine two cutting edge developments in information processing, machine learning and quantum computing, into a radically new technology. In contrast to established machine learning approaches that emulate neural function in software on conventional von Neumann hardware, neuromorphic quantum hardware can offer a significant advantage as it can b e trained on multiple batches of real world data in parallel. This feature is expected to lead to a quantum advantage. Moreover, our approach of implementing neuromorphic quantum hardware is very promising since there exist indications that a quantum advantage in machine learning can already be achieved with moderate fault tolerance. In a longer term perspective neuromorphic hardware architectures will become extremely important in both, classical and quantum computing, particularly for distributed and embedded computing tasks, where the vast scaling of existing architectures does not provide a long-term solution. Quromorphic aims to provide proof of concept demonstrations of this new technology and a roadmap for the path towards its exploitation. To achieve this breakthrough, we will implement two classes of quantum neural networks that have immediate applications in quantum machine learning, feed forward networks and non-equilibrium quantum annealers. This effort will be completed by the development of strategies for scaling the devices to the threshold where they will surpass the capabilities of existing machine learning technology and achieve quantum advantage. In preparation for future exploitation of this new technology, we will run simulations to explore its application to real world problems.

Campo scientifico

Parole chiave

Programma(i)

Argomento(i)

Invito a presentare proposte

H2020-FETOPEN-2018-2020

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Bando secondario

H2020-FETOPEN-2018-2019-2020-01

Meccanismo di finanziamento

RIA - Research and Innovation action

Coordinatore

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN-NUERNBERG
Contribution nette de l'UE
€ 612 482,50
Indirizzo
Schlossplatz 4
91054 Erlangen
Germania

Mostra sulla mappa
Regione
Bayern Mittelfranken Erlangen, Kreisfreie Stadt
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Altri finanziamenti
€ 0,00

Partecipanti (7)

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Ultimo aggiornamento: 29 Aprile 2023

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Permalink: https://cordis.europa.eu/project/id/828826/it

European Union, 2023