Sesto Fiorentino

Jan 6, 2025 | chemistry-europe.onlinelibrary.wiley.com | Umar Farooq |Sesto Fiorentino |Yanping Song |Ali Hassan

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Oct 6, 2023 | frontiersin.org | Sesto Fiorentino

Alessia Vignoli1, 2* Leonardo Tenori1, 2, 3* 1Magnetic Resonance Center, University of Florence, Italy 2Department of Chemistry “Ugo Schiff”, University of Florence, Italy 3Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine, Italy The final, formatted version of the article will be published soon.

Jun 6, 2023 | frontiersin.org | Sesto Fiorentino

Aida Raio1 Federico Brilli1 Luisa Neri2 Rita Baraldi2 Francesca Orlando3 Claudio Pugliesi3 Xiaoyulong Chen4 Ivan Baccelli1* 1Institute for Sustainable Plant Protection, National Research Council of Italy, Italy 2Institute for Bioeconomy, Department of Biology, Agriculture and Food Sciences, National Research Council (CNR), Italy 3Department of Agricultural, Food and Agri-Environmental Sciences, University of Pisa, Italy 4College of Agriculture, Guizhou University, China The final, formatted...

May 9, 2023 | link.aps.org | Jason Pereira |Leonardo Banchi |Sesto Fiorentino |Stefano Pirandola

Abstract Two types of errors can occur when discriminating pairs of quantum states. Asymmetric state discrimination involves minimizing the probability of one type of error, subject to a constraint on the other. We give explicit expressions bounding the set of achievable errors, using the trace norm, the fidelity, and the quantum Chernoff bound. The upper bound is asymptotically tight and the lower bound is exact for pure states.

May 9, 2023 | link.aps.org | Jason Pereira |Sesto Fiorentino |Leonardo Banchi |Stefano Pirandola

Abstract Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we first have to collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious whether the advantage we gain is robust enough to survive data-processing steps such as clustering.