Q-ANNTENNA · Quantum Artificial Neural Networks with Tensor Network Algorithmus
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2017-04-01 → 2019-03-31
- EU contribution
- €159,461
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Quantum Artificial Neural Networks with Tensor Network Algorithmus
During the last decades, quantum information processing has gained tremendous momentum: encoding information in quantum particles has opened a vast array of possibilities and, although the vision of building a scalable universal quantum computer is drawing nearer, many quantum technologies have already reached remarkable degrees of maturity, some of which have already percolated to the industry. On the other hand, our society is producing vast amounts of ever-increasing data, growing at an astronomical rate, rendering traditional data processing methods obsolete and leading to the advent of modern machine learning, resulting in key advances for the processing and interpretation of these data sets. This has triggered a revolution in areas so diverse as computer vision, medical diagnosis, voice recognition, spam filtering or search engines, each having a direct impact onto our society and quality of life. A novel field, quantum machine learning, has emerged from the intersection of the two disciplines, generating a promising symbiosis: quantum resources have the potential to provide the speedup needed to take machine learning to the next level, and ideas from quantum information can in turn be applied to obtain better classical, quantum-inspired algorithms. Interestingly, much of our current understanding of the techniques that underlie the last revolution of modern machine learning have their roots in insights gained from condensed matter and statistical physics and quantum information. The overall objectives of Q-ANNTENNA are to provide a better understanding among these different fields, bringing together the insights in quantum information that the fellow gained during his PhD and the world leading expertise in many-body physics and quantum computation of the host group, envisioning their application in the novel exciting field of quantum machine learning.
Data: CORDIS, © European Union
Project objective
During the recent years, modern machine learning (ML) has sparked a revolution in areas so diverse as computer vision, voice recognition, medical diagnosis and finance. On its own, quantum information processing (QIP) has also gained tremendous momentum and a novel field, quantum machine learning (QML), has emerged from the intersection of the two disciplines. Interestingly, much of the understanding underlying the last revolution of ML is strongly connected to insights gained from condensed matter and statistical physics. It is then natural to use well-understood techniques to describe quantum many-body systems, namely, tensor networks (TN) in the context of ML.The objective of Q-ANNTENNA is to develop a thorough understanding between ML processes and TN. The action will involve state-of-the-art theoretical research at the frontiers of QIP, TN and ML: (1) describing ML processes within the TN formalism, (2) importing TN insights into ML, (3) studying the renormalization process and (4) assessing physical implementations.The experienced researcher, Dr. Jordi Tura, is an expert in QIP in many-body systems. The supervisor, Prof. J. I. Cirac, is a world-expert in QIP, TN and quantum computation, head of the Theory division of the Max Planck Institute of Quantum Optics (MPQ) since 2001.The combined expertise between the fellow and the host is uniquely suited to establish Q-ANNTENNA as a ground-breaking framework for understanding the connections between QIP, ML and TN. The action has a tremendous potential impact onto industry and prospects for patents are likely. In addition, it will contribute to EU’s excellence in QML research, where North America is leading industrial and scientific efforts –by far.The action will greatly increase the applicant’s mobility within EU, create a large network of collaborators for him and MPQ and shape his future career options, with the long-term goal of becoming an independent scientist and establishing his own research group.
Original text from CORDIS.
Participants
- MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV · MUNCHENCoordinatorGermany
Links
Data: CORDIS, © European Union