7 citations · 7 across the 1 of their papers we have counts for
6 papers
Active Learning for the Optimal Design of Multinomial Classification in Physics
Yongcheng Ding, José D. Martín-Guerrero, Yujing Song +2
Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according…
Quantum Pattern Recognition in Photonic Circuits
Rui Wang, Carlos Hernani-Morales, José D. Martín-Guerrero +2
This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patt…
Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images
Oscar J. Pellicer-Valero, José L. Marenco Jiménez, Victor Gonzalez-Perez +7
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…
Experimentally Realizing Efficient Quantum Control with Reinforcement Learning
Ming-Zhong Ai, Yongcheng Ding, Yue Ban +7
Robust and high-precision quantum control is crucial but challenging for scalable quantum computation and quantum information processing. Traditional adiabatic control suffers seve…
Breaking Adiabatic Quantum Control with Deep Learning
Yongcheng Ding, Yue Ban, José D. Martín-Guerrero +3
In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep rei…
Retrieving Quantum Information with Active Learning
Yongcheng Ding, José D. Martín-Guerrero, Mikel Sanz +3
Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides a…