QTML 2017: Quantum Tecniques in Machine Learning Department of Computer Science - University of Verona Verona, Italy, November 6-8, 2017 |
Conference website | http://qtml2017.di.univr.it |
Submission link | https://easychair.org/conferences/?conf=qtml2017 |
Submission deadline | September 16, 2017 |
Quantum Computing (QC) has been for a long time known only for a restricted set of applications where it allows for the achievement of an exponential speed up over the classical computer (e.g. the simulation of quantum physics and chemistry, and the factorisation of large numbers). Recently, however, new developments have opened up opportunities for the application of quantum algorithms to the field of Machine Learning (ML) that may solve problems such as clustering, classification, and pattern matching faster than their classical counterparts. This includes new algorithmic techniques based on Topological Quantum Computation which seem to be especially suitable for kernel-based pattern recognition. The prospects that near term quantum devices could be able to solve computationally hard problems in ML has given rise to Quantum Machine Learning (QML) as a research field in its own right at the intersection between QC and ML. It includes quantum optimisation where theoretical and empirical analysis of quantum annealing approaches are currently subject of intense study.
The QTML 2017 workshop aims to set up a common ground where students and leading researchers in both Quantum Computing and Machine Learning can meet and exchange ideas on the topics of the workshop and discuss how the results from one field can help solving the problems in the other field and viceversa.
Submission Guidelines
Authors are invited to submit extended abstracts of up 2-page length describing work in progress or a summary of a full paper already published or submitted for publication or published on arXiv.org. The presentations included in the QTML 2017 workshop will be selected according to significance, clarity and relevance for the workshop topics.
List of Topics
- Quantum computing for enhancing machine learning algorithms
- Machine learning techniques for the analysis of interacting quantum systems
- Quantum entanglement and topology for the efficient representation of quantum systems
- Topological approaches to machine learning based on Topological Quantum Computation
- Advances of algorithmic techniques for quantum optimisation systems (e.g. quantum annealers)
Committees
Program Committee
- Alberto Carlini (Università del Piemonte Orientale)
- Alessandra Di Pierro (Università di Verona)
- Stefano Mancini (Università di Camerino)
- Simone Severini (UCL London, UK)
- David Windridge (Middlesex University London)
Organizing committee
- Alessandra Di Pierro
- Stefano Mancini
- Riccardo Mengoni
Invited Speakers
- Hans J. Briegel (Universität Innsbruck, Austria)
- Seth Lloyd (Massachusetts Institute of Technology, Boston)
- Marco Loog (TU Delft, Nederlands)
- Hà Quang Minh (Italian Institute of Technology, Genoa, Italy)
- Jiannis Pachos (University of Leeds, UK)
- Francesco Petruccione (University of KwaZulu-Natal, South Africa)
- Davide Venturelli (NASA Intelligent System Division)
- Andreas Winter (Universitat Autònoma de Barcelona, Spain)
- Peter Wittek (Institute for Photonic Science ICFO)
Publication
Accepted abstracts will be collected in informal proceedings that will be distributed at the workshop.
After the workshop, authors will be invited to write a full paper on their work to be submitted for publication in a dedicated volume of the International Journal of Quantum Information (World Scientific Publishing).
Venue
The conference will be held at the Department of Computer Science - University of Verona.
Address:
Strada le Grazie, 15
Ca' Vignal 2,
37134 Verona (Italy)
Tel: +39 045 802 7971
Fax: +39 045 802 7068
Contact
All questions about submissions should be emailed to Alessandra Di Pierro
Sponsors
Department of Computer Science - University of Verona