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pro vyhledávání: '"Mujal, A."'
Quantum reservoir computing (QRC) exploits the dynamical properties of quantum systems to perform machine learning tasks. We demonstrate that optimal performance in QRC can be achieved without relying on disordered systems. Systems with all-to-all to
Externí odkaz:
http://arxiv.org/abs/2411.13401
Quantum measurements affect the state of the observed systems via back-action. While projective measurements extract maximal classical information, they drastically alter the system. In contrast, weak measurements balance information extraction with
Externí odkaz:
http://arxiv.org/abs/2411.03979
Autor:
Mortimer, Luke, Farina, Donato, Di Bello, Grazia, Jansen, David, Leitherer, Andreas, Mujal, Pere, Acín, Antonio
Estimating steady state properties of open quantum systems is a crucial problem in quantum technology. In this work, we show how to derive in a scalable way using semi-definite programming certified bounds on the expectation value of an arbitrary obs
Externí odkaz:
http://arxiv.org/abs/2410.13646
Autor:
Mujal, Pere, Martínez-Peña, Rodrigo, Giorgi, Gian Luca, Soriano, Miguel C., Zambrini, Roberta
Publikováno v:
npj Quantum Inf 9, 16 (2023)
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:
http://arxiv.org/abs/2205.06809
Autor:
Stephan N. F. Spiekman, Martín D. Ezcurra, Adam Rytel, Wei Wang, Eudald Mujal, Michael Buchwitz, Rainer R. Schoch
Publikováno v:
Swiss Journal of Palaeontology, Vol 143, Iss 1, Pp 1-33 (2024)
Abstract Some of the earliest members of the archosaur-lineage (i.e., non-archosauriform archosauromorphs) are characterised by an extremely elongated neck. Recent fossil discoveries from the Guanling Formation (Middle Triassic) of southern China hav
Externí odkaz:
https://doaj.org/article/8a2aac52c2914f5b9ab4395c0a3122b9
Autor:
Mujal, Pere
Publikováno v:
Condens. Matter 2022, 7(1), 17
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:
http://arxiv.org/abs/2201.11096
Autor:
Mujal, Pere, Martínez-Peña, Rodrigo, Nokkala, Johannes, García-Beni, Jorge, Giorgi, Gian Luca, Soriano, Miguel C., Zambrini, Roberta
Publikováno v:
Adv. Quantum Technol. 2100027 (2021)
Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems c
Externí odkaz:
http://arxiv.org/abs/2102.11831
Autor:
Joep Schaeffer, Ewan Wolff, Florian Witzmann, Gabriel S Ferreira, Rainer R Schoch, Eudald Mujal
Publikováno v:
PLoS ONE, Vol 19, Iss 7, p e0306819 (2024)
Paleopathology, the study of diseases and injuries from the fossil record, allows for a unique view into the life of prehistoric animals. Pathologies have nowadays been described in nearly all groups of fossil vertebrates, especially dinosaurs. Despi
Externí odkaz:
https://doaj.org/article/2b149d8be772421d90b6c00f168a103a
Publikováno v:
SciPost Phys. 10, 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 differen
Externí odkaz:
http://arxiv.org/abs/2010.03875
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