1 code implementation • 10 Sep 2023 • Carlos Hernani-Morales, Gabriel Alvarado, Francisco Albarrán-Arriagada, Yolanda Vives-Gilabert, Enrique Solano, José D. Martín-Guerrero
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors.
no code implementations • 8 Sep 2023 • Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales, José J. Orquín-Marqués, Narendra N. Hegade, Alejandro Gomez Cadavid, Iraitz Montalban, Enrique Solano, Yolanda Vives-Gilabert, José D. Martín-Guerrero
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.
no code implementations • 8 Jul 2023 • Yongcheng Ding, José D. Martín-Guerrero, Yolanda Vives-Gilabert, Xi Chen
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence.
1 code implementation • 23 Mar 2021 • Oscar J. Pellicer-Valero, José L. Marenco Jiménez, Victor Gonzalez-Perez, Juan Luis Casanova Ramón-Borja, Isabel Martín García, María Barrios Benito, Paula Pelechano Gómez, José Rubio-Briones, María José Rupérez, José D. Martín-Guerrero
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy.
1 code implementation • 11 Apr 2019 • Ana Martin, Bruno Candelas, Ángel Rodríguez-Rozas, José D. Martín-Guerrero, Xi Chen, Lucas Lamata, Román Orús, Enrique Solano, Mikel Sanz
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates.
Quantum Physics Mesoscale and Nanoscale Physics
1 code implementation • 7 Mar 2019 • Gorka Muñoz-Gil, Miguel Angel Garcia-March, Carlo Manzo, José D. Martín-Guerrero, Maciej Lewenstein
In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy.
1 code implementation • 14 Feb 2019 • Raúl V. Casaña-Eslava, Paulo J. G. Lisboa, Sandra Ortega-Martorell, Ian H. Jarman, José D. Martín-Guerrero
However, it is very sensitive to a length parameter that is inherent to the Schr\"odinger equation.
no code implementations • 16 Dec 2016 • Unai Alvarez-Rodriguez, Lucas Lamata, Pablo Escandell-Montero, José D. Martín-Guerrero, Enrique Solano
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations.
no code implementations • 14 Sep 2015 • Pablo Escandell-Montero, Milena Chermisi, José M. Martínez-Martínez, Juan Gómez-Sanchis, Carlo Barbieri, Emilio Soria-Olivas, Flavio Mari, Joan Vila-Francés, Andrea Stopper, Emanuele Gatti, José D. Martín-Guerrero
Results: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level.