The paper entitled ‘Single trajectory characterization via machine learning’ co-authored by IDAL member José D. Martín-Guerrero has been selected by the publisher as one the top five published in 2020 according to its quality, interest awakaned in readers, downloads and citations. The paper proposes a Machine Learning approach to accurately characterize single trajectories in diffusion mechanisms. The research was carried out in collaboration with the Research Institute for Photonic Sciences, ICFO.
Category: Papers
IDAL member Prof. José D. Martín-Guerrero, and Prof. Dr. Lucas Lamata, from Universidad de Sevilla, are Guest Editors of a special issue in the journal Applied Sciences on Machine Learning and Physics. The deadline is 31st October 2021. We are welcoming original research papers, mini-reviews and perspective articles dealing with aspects in the fuzzy border between Physics and Machine Learning, particularly Quantum Machine Learning, Physics-inspired Machine Learning algorithms and applications of classical (and quantum) Machine Learning for different Physics applications. More info at https://www.mdpi.com/journal/applsci/special_issues/Machine_Learning_Physics.
IDAL member Prof. José D. Martín and Prof. Lucas Lamata – University of Seville – will be organizing a special session on Quantum Machine Learning at the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, that will be held in Bruges, Belgium from 22nd to 24th April 2020. The deadline for paper submission is November 18th. We are looking forward to receiving your contributions! More info at https://www.esann.org/special-sessions#session6
The paper entitled “Towards Pricing Financial Derivatives with an IBM Quantum Computer”, co-authored by IDAL member José D. Martín has been selected as the best one of the week in arXiv Physics by MIT Technology Review. The paper makes use of a quantum computer to simulate and estimate the price of financial derivatives in realistic scenarios, opening a window to the use of quantum computing in Economics: https://www.technologyreview.com/s/613339/the-best-of-the-physics-arxiv-week-ending-april-20-2019/
The paper entitled “Supervised Quantum Learning without Measurements”, and co-authored by IDAL member José D. Martín and former IDAL member Pablo Escandell has been rated as one of the most popular ones of 2017. In particular, according to Nature Publishing Group the paper is the 26th paper with most views in 2017 (out of more than 3,000 papers published about Physics): https://www.nature.com/collections/lbnlhhghdx/content/26-50
This is especially remarkable as the paper was published in late September, thus being only available in the last two months of the year. This encourages us to carry on and make even more efforts in producing high-quality science that may have a real impact on the research community.
