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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.

Field of science

  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/computer hardware/quantum computer
  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /natural sciences/computer and information sciences/data science/data processing
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning

Call for proposal

H2020-FETOPEN-2018-2019-2020-01
See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

HERIOT-WATT UNIVERSITY
Address
Riccarton
EH14 4AS Edinburgh
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 649 982,50

Participants (5)

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Switzerland
EU contribution
€ 549 375
Address
Raemistrasse 101
8092 Zuerich
Activity type
Higher or Secondary Education Establishments
TECHNISCHE UNIVERSITEIT DELFT
Netherlands
EU contribution
€ 549 802,50
Address
Stevinweg 1
2628 CN Delft
Activity type
Higher or Secondary Education Establishments
IBM RESEARCH GMBH
Switzerland
EU contribution
€ 537 437,50
Address
Saeumerstrasse 4
8803 Rueschlikon
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO UNIBERTSITATEA
Spain
EU contribution
€ 297 250
Address
Barrio Sarriena S N
48940 Leioa
Activity type
Higher or Secondary Education Establishments
VOLKSWAGEN AG
Germany
EU contribution
€ 298 905
Address
Berliner Ring 2
38440 Wolfsburg
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)