Zobrazeno 1 - 10
of 303
pro vyhledávání: '"Bernabe Moreno"'
Autor:
Alexander Tchernik
Publikováno v:
Латиноамериканский исторический альманах, Vol 34, Pp 7-279 (2022)
In the heraldic treatise of B. Moreno de Vargas «Discursos de la noble-za de España» one of the chapters is devoted to the differences that exist between the coats of arms. These are additional, off-board signs that can be called group signs. In f
Externí odkaz:
https://doaj.org/article/f90772f66d6b4080845fe4769fdf13a8
Autor:
Hamann, Hendrik F., Brunschwiler, Thomas, Gjorgiev, Blazhe, Martins, Leonardo S. A., Puech, Alban, Varbella, Anna, Weiss, Jonas, Bernabe-Moreno, Juan, Massé, Alexandre Blondin, Choi, Seong, Foster, Ian, Hodge, Bri-Mathias, Jain, Rishabh, Kim, Kibaek, Mai, Vincent, Mirallès, François, De Montigny, Martin, Ramos-Leaños, Octavio, Suprême, Hussein, Xie, Le, Youssef, El-Nasser S., Zinflou, Arnaud, Belyi, Alexander J., Bessa, Ricardo J., Bhattarai, Bishnu Prasad, Schmude, Johannes, Sobolevsky, Stanislav
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex syst
Externí odkaz:
http://arxiv.org/abs/2407.09434
Autor:
Pekaslan, Direnc, Alonso-Moral, Jose Maria, Bandara, Kasun, Bergmeir, Christoph, Bernabe-Moreno, Juan, Eigenmann, Robert, Einecke, Nils, Ergen, Selvi, Godahewa, Rakshitha, Hewamalage, Hansika, Lago, Jesus, Limmer, Steffen, Rebhan, Sven, Rabinovich, Boris, Rajapasksha, Dilini, Song, Heda, Wagner, Christian, Wu, Wenlong, Magdalena, Luis, Triguero, Isaac
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Tec
Externí odkaz:
http://arxiv.org/abs/2311.04007
Autor:
Ghosh, Kumar J. B., Yogaraj, Kavitha, Agliardi, Gabriele, Sabino, Piergiacomo, Fernández-Campoamor, Marina, Bernabé-Moreno, Juan, Cortiana, Giorgio, Shehab, Omar, O'Meara, Corey
We generalize the Approximate Quantum Compiling algorithm into a new method for CNOT-depth reduction, which is apt to process wide target quantum circuits. Combining this method with state-of-the-art techniques for error mitigation and circuit compil
Externí odkaz:
http://arxiv.org/abs/2305.09501
Autor:
Agliardi, Gabriele, O'Meara, Corey, Yogaraj, Kavitha, Ghosh, Kumar, Sabino, Piergiacomo, Fernández-Campoamor, Marina, Cortiana, Giorgio, Bernabé-Moreno, Juan, Tacchino, Francesco, Mezzacapo, Antonio, Shehab, Omar
Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely risk management demands for efficient simulation of millions of scenario
Externí odkaz:
http://arxiv.org/abs/2304.10385
Akademický článek
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Autor:
Mayer, Kevin, Haas, Lukas, Huang, Tianyuan, Bernabé-Moreno, Juan, Rajagopal, Ram, Fischer, Martin
Current methods to determine the energy efficiency of buildings require on-site visits of certified energy auditors which makes the process slow, costly, and geographically incomplete. To accelerate the identification of promising retrofit targets on
Externí odkaz:
http://arxiv.org/abs/2206.02270
Autor:
Kumar Ghosh, Kavitha Yogaraj, Gabriele Agliardi, Piergiacomo Sabino, Fernandez-Campoamor Marina, Bernabe-Moreno Juan, Giorgio Cortiana, Omar Shehab, Corey O'Meara
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-17 (2024)
In this article, we generalize the approximate quantum compiling algorithm into a new method for cnot-depth reduction, which is apt to process wide target quantum circuits. Combining this method with state-of-the-art techniques for error mitigation a
Externí odkaz:
https://doaj.org/article/05d766722e554080a3d4c8236e20bd82
Autor:
Gómez, Raúl Berganza, O'Meara, Corey, Cortiana, Giorgio, Mendl, Christian B., Bernabé-Moreno, Juan
Publikováno v:
2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), 129-136 (2022)
The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a po
Externí odkaz:
http://arxiv.org/abs/2202.08024
Autor:
Sakhnenko, Alona, O'Meara, Corey, Ghosh, Kumar J. B., Mendl, Christian B., Cortiana, Giorgio, Bernabé-Moreno, Juan
Publikováno v:
Quantum Mach. Intell. 4, 27 (2022)
We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard ou
Externí odkaz:
http://arxiv.org/abs/2112.08869