Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Davide Pastorello"'
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-12 (2024)
Support vector machines (SVMs) are widely used machine learning models, with formulations for both classification and regression tasks. In the last years, with the advent of working quantum annealers, hybrid SVM models characterized by quantum traini
Externí odkaz:
https://doaj.org/article/f3d02ddba02d4efb952aeb4dd37c95c9
Autor:
Roberto Leporini, Davide Pastorello
Publikováno v:
Quantum Reports, Vol 4, Iss 4, Pp 434-441 (2022)
In quantum machine learning, feature vectors are encoded into quantum states. Measurements for the discrimination of states are useful tools for classification problems. Classification algorithms inspired by quantum state discrimination have recently
Externí odkaz:
https://doaj.org/article/854bfa8f6ea84175a750d233d99ffb51
Autor:
Roberto Leporini, Davide Pastorello
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Optimal measurements for the discrimination of quantum states are useful tools for classification problems. In order to exploit the potential of quantum computers, feature vectors have to be encoded into quantum states represented by density
Externí odkaz:
https://doaj.org/article/991055c1fd374643bbe48d1442fdade7
Publikováno v:
PLoS ONE, Vol 18, Iss 11, p e0287869 (2023)
In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which al
Externí odkaz:
https://doaj.org/article/feae80ef0dfa413dab0c351adaf6eaf3
Autor:
Roberto Leporini, Davide Pastorello
Publikováno v:
Quantum Reports, Vol 3, Iss 3, Pp 482-499 (2021)
We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vect
Externí odkaz:
https://doaj.org/article/479ebbfb069d48ac9fc8be8295fd1f14
Autor:
Davide Pastorello
Publikováno v:
Concise Guide to Quantum Machine Learning ISBN: 9789811968969
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab6f3f655886634b02cb91dfecb78aef
https://doi.org/10.1007/978-981-19-6897-6_3
https://doi.org/10.1007/978-981-19-6897-6_3
Autor:
Davide Pastorello
Publikováno v:
Concise Guide to Quantum Machine Learning ISBN: 9789811968969
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f7f68d2d9e803084690129f061b76ef
https://doi.org/10.1007/978-981-19-6897-6_9
https://doi.org/10.1007/978-981-19-6897-6_9
Autor:
Davide Pastorello
Publikováno v:
Concise Guide to Quantum Machine Learning ISBN: 9789811968969
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5da055afeca7e33211550271f176b1cf
https://doi.org/10.1007/978-981-19-6897-6_2
https://doi.org/10.1007/978-981-19-6897-6_2
Autor:
Davide Pastorello
Publikováno v:
Concise Guide to Quantum Machine Learning ISBN: 9789811968969
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa1935f63ba8f5e7cbb1e495419a6c3f
https://doi.org/10.1007/978-981-19-6897-6_8
https://doi.org/10.1007/978-981-19-6897-6_8
Autor:
Davide Pastorello
Publikováno v:
Concise Guide to Quantum Machine Learning ISBN: 9789811968969
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8cc4487398e779861b6ea591fbfb77f
https://doi.org/10.1007/978-981-19-6897-6_1
https://doi.org/10.1007/978-981-19-6897-6_1