Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Andre Sequeira"'
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-11 (2024)
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC
Externí odkaz:
https://doaj.org/article/e5050aba5e83488686c7b85f3d802c6c
Publikováno v:
IEEE Access, Vol 9, Pp 125416-125427 (2021)
Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneously, we are witnessing the emergence of a new field: Quantum Machine Learning. In the context of these two major developments, this work addresses the
Externí odkaz:
https://doaj.org/article/4c86c24cc6cf40f9bc7a67baed18eb69
Publikováno v:
IEEE Access, Vol 9, Pp 125416-125427 (2021)
Reinforcement Learning is at the core of a recent revolution in Arti cial Intelligence. Simultaneously, we are witnessing the emergence of a new eld: Quantum Machine Learning. In the context of these two major developments, this work addresses the in
Publikováno v:
Q-SE@ICSE
This extended abstract reports on on-going research on quantum algorithmic approaches to the problem of generalised tree search that may exhibit effective quantum speedup, even in the presence of non-constant branching factors. Two strategies are bri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::640702eb82e55b1a5e3e1c79686a46bf
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035037 (2024)
This research explores the trainability of Parameterized Quantum Circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gr
Externí odkaz:
https://doaj.org/article/e73ce4fb161543efacc5d59b27b541e3