2 citations · 2 across the 3 of their papers we have counts for
5 papers
Kerr Quasinormal Modes without Variable Separation: A Two-Dimensional Hyperboloidal Teukolsky Solver with Physics-Informed Neural Networks
Antonio Ferrer-Sánchez, Daniela D. Doneva, José D. Martín-Guerrero +4
We use physics-informed neural networks (PINNs) to solve the gravitational quasinormal-mode (QNM) eigenvalue problem for Kerr spacetime directly in the two-dimensional hyperboloida…
Analog Quantum Feature Selection with Neutral-Atom Quantum Processors
Jose J. Orquin-Marques, Carlos Flores-Garrigos, Alejandro Gomez Cadavid +5
We present a quantum-native approach to quantum feature selection (QFS) based on analog quantum simulation with neutral atom arrays, adaptable to a variety of academic and industri…
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales +7
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…
Machine Learning for maximizing the memristivity of single and coupled quantum memristors
Carlos Hernani-Morales, Gabriel Alvarado, Francisco Albarrán-Arriagada +3
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…
Active Learning in Physics: From 101, to Progress, and Perspective
Yongcheng Ding, José D. Martín-Guerrero, Yolanda Vives-Gilabert +1
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 sa…