Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Rodrigo Martínez-Peña"'
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of re
Externí odkaz:
https://doaj.org/article/91d219ee34ee4718842f3eda0cdbd1d1
Autor:
Tilman Hertz, Thomas Banitz, Rodrigo Martínez-Peña, Sonja Radosavljevic, Emilie Lindkvist, Lars-Göran Johansson, Petri Ylikoski, Maja Schlüter
Publikováno v:
Ecology and Society, Vol 29, Iss 1, p 14 (2024)
Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves div
Externí odkaz:
https://doaj.org/article/f7a8bb6bfe4449eba0da2738d9338cf6
Publikováno v:
Quantum, Vol 8, p 1291 (2024)
Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource. We show the potential enhancement induced by dissipation in the field
Externí odkaz:
https://doaj.org/article/97914b31bca4473d8dccbcb99660ba10
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:
Johannes Nokkala, Rodrigo Martínez-Peña, Gian Luca Giorgi, Valentina Parigi, Miguel C. Soriano, Roberta Zambrini
Publikováno v:
Communications Physics, Vol 4, Iss 1, Pp 1-11 (2021)
Most attempts to delineate quantum machine-learning-related computing capabilities of continuous variables states have relied on non-Gaussian resources. Here, the authors show that linear systems with continuous-variable Gaussian states are a promisi
Externí odkaz:
https://doaj.org/article/6d99dbbbdff343148f879fffd11fbb76
Autor:
Lars-Göran Johansson, Thomas Banitz, Volker Grimm, Tilman Hertz, Emilie Lindkvist, Rodrigo Martínez Peña, Sonja Radosavljevic, Petri Ylikoski, Maja Schlüter
This open access book is about causal thinking and the use of causal language, with a focus on introducing philosophical ideas about causation to students and researchers of Social-Ecological Systems (SES). It takes a systematic approach to three cen
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:2664-2675
Reservoir computing has emerged as a powerful machine learning paradigm for harvesting nontrivial information processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become nonlinear functio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71559f480f328d98dd03e09046ab0637
https://doi.org/10.1109/tnnls.2021.3105695
https://doi.org/10.1109/tnnls.2021.3105695
Most existing results in the analysis of quantum reservoir computing (QRC) systems with classical inputs have been obtained using the density matrix formalism. This paper shows that alternative representations can provide better insights when dealing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::017ec2427705a5438cf48e26b6d31bd7
http://arxiv.org/abs/2212.00396
http://arxiv.org/abs/2212.00396
Autor:
Miguel C. Soriano, Gian Luca Giorgi, Valentina Parigi, Rodrigo Martínez-Peña, Roberta Zambrini, Johannes Nokkala
Publikováno v:
Communications Physics, Vol 4, Iss 1, Pp 1-11 (2021)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Communications Physics
Communications Physics, Nature Research, 2021, 4 (1), ⟨10.1038/s42005-021-00556-w⟩
Digital.CSIC: Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Communications Physics
Communications Physics, Nature Research, 2021, 4 (1), ⟨10.1038/s42005-021-00556-w⟩
Digital.CSIC: Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
We establish the potential of continuous-variable Gaussian states of linear dynamical systems for machine learning tasks. Specifically, we consider reservoir computing, an efficient framework for online time series processing. As a reservoir we consi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::231fe1d0c5de11590236fda823c6922a
https://doaj.org/article/6d99dbbbdff343148f879fffd11fbb76
https://doaj.org/article/6d99dbbbdff343148f879fffd11fbb76
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