Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Pere Mujal"'
Publikováno v:
npj Quantum Information, Vol 9, Iss 1, Pp 1-10 (2023)
Abstract Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine le
Externí odkaz:
https://doaj.org/article/50502e1f1e2b4cbf9164672f4f57edb7
Autor:
Pere Mujal
Publikováno v:
Condensed Matter, Vol 7, Iss 1, p 17 (2022)
Quantum reservoir computing is a machine learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the rese
Externí odkaz:
https://doaj.org/article/b075fc25290c465aab38f71dbc763b51
Publikováno v:
SciPost Physics, Vol 10, Iss 3, p 073 (2021)
We investigate the supervised machine learning of few interacting bosons in optical speckle disorder via artificial neural networks. The learning curve shows an approximately universal power-law scaling for different particle numbers and for diffe
Externí odkaz:
https://doaj.org/article/e06efaaeec1c4535a047d584884323ee
Quantum machine learning represents a promising avenue for data processing, also for purposes of sequential temporal data analysis, as recently proposed in quantum reservoir computing (QRC). The possibility to operate on several platforms and noise i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49b574e1f188af15bb351644dc95b71d
http://arxiv.org/abs/2205.06809
http://arxiv.org/abs/2205.06809
Autor:
Pere Mujal Torreblanca
Publikováno v:
Condensed Matter; Volume 7; Issue 1; Pages: 17
Digital.CSIC. Repositorio Institucional del CSIC
instname
Digital.CSIC. Repositorio Institucional del CSIC
instname
Quantum reservoir computing is a machine-learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the rese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30ea6a9e0cab0f761624e0cec338e749
http://arxiv.org/abs/2201.11096
http://arxiv.org/abs/2201.11096
Autor:
Roberta Zambrini, Johannes Nokkala, Miguel C. Soriano, Rodrigo Martínez-Peña, Pere Mujal, Gian Luca Giorgi
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
instname
The natural dynamics of complex networks can be harnessed for information processing purposes. A paradigmatic example are artificial neural networks used for machine learning. In this context, quantum reservoir computing (QRC) constitutes a natural e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d80ccad3547670b912c450199126841
http://hdl.handle.net/10261/266704
http://hdl.handle.net/10261/266704
Publikováno v:
Condensed Matter, Vol 3, Iss 1, p 9 (2018)
The system of two interacting bosons in a two-dimensional harmonic trap is compared with the system consisting of two noninteracting fermions in the same potential. In particular, we discuss how the properties of the ground state of the system, e.g.,
Externí odkaz:
https://doaj.org/article/6d3cff8f04404084ac13b8b8cc161d21
Autor:
Gian Luca Giorgi, Miguel C. Cornelles-Soriano, Roberta Zambrini, Johannes Nokkala, Pere Mujal, Jorge Garcia Beni, Rodrigo Martínez-Peña
Publikováno v:
Emerging Topics in Artificial Intelligence (ETAI) 2021.
Quantum reservoir computing is an unconventional computing approach that exploits the quantumness of physical systems used as reservoirs to process information, combined with an easy training strategy. An overview is presented about a range of possib
Autor:
Gian Luca Giorgi, Miguel C. Soriano, Johannes Nokkala, Rodrigo Martínez-Peña, Roberta Zambrini, Pere Mujal, Jorge García-Beni
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
instname
Quantum reservoir computing and quantum extreme learning machines are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3aa45714691cd37de4cd621194167797
http://arxiv.org/abs/2102.11831
http://arxiv.org/abs/2102.11831
The disorder-induced localization of few bosons interacting via a contact potential is investigated through the analysis of the level-spacing statistics familiar from random matrix theory. The model we consider is defined in a continuum and describes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6ee8acd83c56d1657bf637f3f7747db
http://arxiv.org/abs/1903.03373
http://arxiv.org/abs/1903.03373