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Guided quantum compression for high dimensional data classification / Belis, Vasilis (Zurich, ETH) ; Odagiu, Patrick (Zurich, ETH) ; Grossi, Michele (CERN) ; Reiter, Florentin (Zurich, ETH) ; Dissertori, Günther (Zurich, ETH) ; Vallecorsa, Sofia (CERN)
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. [...]
arXiv:2402.09524.- 2024-07-16 - 11 p. - Published in : Mach. Learn. Sci. Tech. 5 (2024) 035010 Fulltext: document - PDF; 2402.09524 - PDF;

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Quantum anomaly detection in the latent space of proton collision events at the LHC / Belis, Vasilis (Zurich, ETH) ; Woźniak, Kinga Anna (CERN ; Vienna U.) ; Puljak, Ema (CERN ; Barcelona U.) ; Barkoutsos, Panagiotis (IBM, Zurich) ; Dissertori, Günther (Zurich, ETH) ; Grossi, Michele (CERN) ; Pierini, Maurizio (CERN) ; Reiter, Florentin (Zurich, ETH) ; Tavernelli, Ivano (IBM, Zurich) ; Vallecorsa, Sofia (CERN)
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in the enhancement of experimental capabilities. [...]
arXiv:2301.10780.- 2024-10-14 - 11 p. - Published in : Commun. Phys. 7 (2024) 334 Fulltext: 2301.10780 - PDF; document - PDF;

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Higgs analysis with quantum classifiers / Belis, Vasileios (ETH, Zurich (main)) ; González-Castillo, Samuel (Oviedo U.) ; Reissel, Christina (ETH, Zurich (main)) ; Vallecorsa, Sofia (CERN) ; Combarro, Elías F. (Oviedo U.) ; Dissertori, Günther (ETH, Zurich (main)) ; Reiter, Florentin (Zurich, ETH-CSCS/SCSC)
We have developed two quantum classifier models for the $t\bar{t}H(b\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. [...]
arXiv:2104.07692.- 2021 - 12 p. - Published in : EPJ Web Conf. 251 (2021) 03070 Fulltext: 2104.07692 - PDF; document - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.03070

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Također vidi: slična imena autora
3	Reiter, F
2	Reiter, F W

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