In the last few years, we have witnessed an increasing interest in bridging two impor- tant research areas that fundamentally changed our way and abilities of processing information, namely Machine Learning and Quantum Computation. In the Summer 2017, we had the idea of inviting major experts in Quantum Compu- tation and Information on the one hand, and in Machine Learning and Optimisation on the other hand, for a meeting at the University of Verona to discuss the latest advances in the newly born field of Quantum Machine Learning. The idea developed in a very successful workshop, bringing together more than hun- dred scientists to attend and/or contribute their results on the two-way interaction between Machine Learning and Quantum Computation, aimed at demonstrating how the intersection of the two fields can offer great potential for both. This special issue is dedicated to this event, which was held in Verona on 6-8 November 2017 under the name of QTML 2017 - 1st Workshop on Quantum Techniques in Machine Learning. It represents the first of a series of workshop that are now held yearly in diverse places worldwide. The volume collects original contributions focused on the following topics and not limited to the works presented at QTML 2017: - 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 quan- tum systems - Approaches to machine learning based on Topological Quantum Computation - Algorithmic techniques for quantum optimisation (e.g. quantum annealing). We wish to thank all the people who contributed to bringing this special issue to completion. In particular, we are very grateful to the reviewers who provided a valuable help to ensure the high quality of the papers, to the authors for the scrupu- lous work in improving their papers following the reviewers' suggestions, and to all the participants in QTML 2017 whose lively discussions and critical interventions greatly inspired the authors.

Quantum Techniques in Machine Learning

Mancini S.
2018

Abstract

In the last few years, we have witnessed an increasing interest in bridging two impor- tant research areas that fundamentally changed our way and abilities of processing information, namely Machine Learning and Quantum Computation. In the Summer 2017, we had the idea of inviting major experts in Quantum Compu- tation and Information on the one hand, and in Machine Learning and Optimisation on the other hand, for a meeting at the University of Verona to discuss the latest advances in the newly born field of Quantum Machine Learning. The idea developed in a very successful workshop, bringing together more than hun- dred scientists to attend and/or contribute their results on the two-way interaction between Machine Learning and Quantum Computation, aimed at demonstrating how the intersection of the two fields can offer great potential for both. This special issue is dedicated to this event, which was held in Verona on 6-8 November 2017 under the name of QTML 2017 - 1st Workshop on Quantum Techniques in Machine Learning. It represents the first of a series of workshop that are now held yearly in diverse places worldwide. The volume collects original contributions focused on the following topics and not limited to the works presented at QTML 2017: - 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 quan- tum systems - Approaches to machine learning based on Topological Quantum Computation - Algorithmic techniques for quantum optimisation (e.g. quantum annealing). We wish to thank all the people who contributed to bringing this special issue to completion. In particular, we are very grateful to the reviewers who provided a valuable help to ensure the high quality of the papers, to the authors for the scrupu- lous work in improving their papers following the reviewers' suggestions, and to all the participants in QTML 2017 whose lively discussions and critical interventions greatly inspired the authors.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11581/420935
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