Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Alessandra DI PIERRO"'
Autor:
Massimiliano Incudini, Michele Grossi, Antonio Mandarino, Sofia Vallecorsa, Alessandra Di Pierro, David Windridge
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 4, Pp 1-16 (2023)
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent nonlinearity of classical deep learning, a problem in the quantum domain due to the fact t
Externí odkaz:
https://doaj.org/article/74b8c0cb20c14b0abb5b3380e643e6bc
Autor:
Alessandra Di Pierro, Herbert Wiklicky
Publikováno v:
Electronic Proceedings in Theoretical Computer Science, Vol 119, Iss Proc. GandALF 2013, Pp 150-165 (2013)
Speculative optimisation relies on the estimation of the probabilities that certain properties of the control flow are fulfilled. Concrete or estimated branch probabilities can be used for searching and constructing advantageous speculative and bookk
Externí odkaz:
https://doaj.org/article/7607a159b18249ae997e154f1b01b121
Autor:
Massimiliano Incudini, Fabio Tarocco, Riccardo Mengoni, Alessandra Di Pierro, Antonio Mandarino
Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types of the data the algorithm is working on. For graph-shaped data, an impo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8187043d2e1e77a584ada79d3f525d6
https://hdl.handle.net/11562/1079569
https://hdl.handle.net/11562/1079569
We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa38ef541b57e6e42236e601fe1be3e6
http://arxiv.org/abs/2102.04823
http://arxiv.org/abs/2102.04823
Publikováno v:
Protocols, Strands, and Logic ISBN: 9783030916305
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::133ef7cfcfb516a22ef95e5bca3b4cba
https://doi.org/10.1007/978-3-030-91631-2_8
https://doi.org/10.1007/978-3-030-91631-2_8
Autor:
Alessandra Di Pierro
Publikováno v:
From Lambda Calculus to Cybersecurity Through Program Analysis ISBN: 9783030411022
We present a theory of types where formulas may contain a choice constructor. This constructor allows for the selection of a particular type among a finite set of options, each corresponding to a given probabilistic term. We show that this theory ind
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6851e504a3d6ba071033602d9a327fd5
https://doi.org/10.1007/978-3-030-41103-9_3
https://doi.org/10.1007/978-3-030-41103-9_3
Autor:
Alessandra Di Pierro, Riccardo Mengoni
Quantum Machine Learning has established itself as one of the most promising applications of quantum computers and Noisy Intermediate Scale Quantum (NISQ) devices. In this paper, we review the latest developments regarding the usage of quantum comput
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13a358aeed306f7e429c944fdfcb3f95
http://hdl.handle.net/11562/1008257
http://hdl.handle.net/11562/1008257
Publikováno v:
Scopus-Elsevier
We introduce a homology-based technique for the classification of multiqubit state vectors with genuine entanglement. In our approach, we associate state vectors to data sets by introducing a metric-like measure in terms of bipartite entanglement, an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2147291a5e8c4e52b92d4ec14a954b31
This Festschrift is in honor of Chris Hankin, Professor at the Imperial College in London, UK, on the Occasion of His 65th Birthday.Chris Hankin is a Fellow of the Institute for Security Science and Technology and a Professor of Computing Science.Hi
We propose the usage of persistent homologies to characterize multipartite entanglement. On a multi-qubit data set we introduce metric-like measures defined in terms of bipartite entanglement and then we derive barcodes. We show that, depending on th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ad19ea49a05a00ca2474c981a6466ea
http://hdl.handle.net/11562/990562
http://hdl.handle.net/11562/990562