Zobrazeno 1 - 10
of 324
pro vyhledávání: '"Martín Guerrero, José"'
Autor:
Freitas, Osvaldo Gramaxo, Theodoropoulos, Anastasios, Villanueva, Nino, Fernandes, Tiago, Nunes, Solange, Font, José A., Onofre, Antonio, Torres-Forné, Alejandro, Martin-Guerrero, José D.
Gravitational wave approximants are widely used tools in gravitational-wave astronomy. They allow for dense coverage of the parameter space of binary black hole (BBH) mergers for purposes of parameter inference, or, more generally, match filtering ta
Externí odkaz:
http://arxiv.org/abs/2412.06946
Autor:
Caron, Sascha, Dobreva, Nadezhda, Sánchez, Antonio Ferrer, Martín-Guerrero, José D., Odyurt, Uraz, Bazan, Roberto Ruiz de Austri, Wolffs, Zef, Zhao, Yue
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step
Externí odkaz:
http://arxiv.org/abs/2407.07179
Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled tra
Externí odkaz:
http://arxiv.org/abs/2405.18230
Autor:
Odyurt, Uraz, Dobreva, Nadezhda, Wolffs, Zef, Zhao, Yue, Sánchez, Antonio Ferrer, Bazan, Roberto Ruiz de Austri, Martín-Guerrero, José D., Varbanescu, Ana-Lucia, Caron, Sascha
Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorith
Externí odkaz:
http://arxiv.org/abs/2405.17325
Autor:
Simen, Anton, Flores-Garrigos, Carlos, Hegade, Narendra N., Montalban, Iraitz, Vives-Gilabert, Yolanda, Michon, Eric, Zhang, Qi, Solano, Enrique, Martín-Guerrero, José D.
We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, a
Externí odkaz:
http://arxiv.org/abs/2405.00548
Publikováno v:
Physical Review A (2024)
In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal design for experiments. This study investigates the extent to which guidance from human
Externí odkaz:
http://arxiv.org/abs/2312.06335
Autor:
Hernani-Morales, Carlos, Alvarado, Gabriel, Albarrán-Arriagada, Francisco, Vives-Gilabert, Yolanda, Solano, Enrique, Martín-Guerrero, José D.
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unvei
Externí odkaz:
http://arxiv.org/abs/2309.05062
Autor:
Ferrer-Sánchez, Antonio, Flores-Garrigos, Carlos, Hernani-Morales, Carlos, Orquín-Marqués, José J., Hegade, Narendra N., Cadavid, Alejandro Gomez, Montalban, Iraitz, Solano, Enrique, Vives-Gilabert, Yolanda, Martín-Guerrero, José D.
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits. The primary object
Externí odkaz:
http://arxiv.org/abs/2309.04434
Publikováno v:
Adv. Quantum Technol. 2023, 2300208
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be ann
Externí odkaz:
http://arxiv.org/abs/2307.03899
Autor:
Ferrer-Sánchez, Antonio, Martín-Guerrero, José D., de Austri, Roberto Ruiz, Torres-Forné, Alejandro, Font, José A.
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified
Externí odkaz:
http://arxiv.org/abs/2305.08448