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Neuromrophic Quantum Computing

Objective

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.
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Coordinator

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN NUERNBERG

Address

Schlossplatz 4
91054 Erlangen

Germany

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 612 482,50

Participants (6)

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EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland

EU Contribution

€ 556 875

TECHNISCHE UNIVERSITEIT DELFT

Netherlands

EU Contribution

€ 557 302,50

IBM RESEARCH GMBH

Switzerland

EU Contribution

€ 544 937,50

UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO UNIBERTSITATEA

Spain

EU Contribution

€ 304 750

VOLKSWAGEN AG

Germany

EU Contribution

€ 306 405

HERIOT-WATT UNIVERSITY

United Kingdom

Project information

Grant agreement ID: 828826

Status

Ongoing project

  • Start date

    1 June 2019

  • End date

    31 May 2022

Funded under:

H2020-EU.1.2.1.

  • Overall budget:

    € 2 882 752,50

  • EU contribution

    € 2 882 752,50

Coordinated by:

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN NUERNBERG

Germany