25 citations · 27 across the 7 of their papers we have counts for
5 papers · 1 filter
The Fourier Wall: Why Public Tabular Datasets Refuse Quantum Advantage, and a Certified Recipe for Where It Lives
Javier Mancilla, Tomás Tagliani
Across public tabular benchmarks, quantum machine-learning (QML) models usually lose to carefully tuned classical baselines. We argue that this is a structural property of the data…
A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
Rodrigo Chaves, Kunal Kumar, Bruno Chagas +4
This paper investigates whether hybrid quantum-classical machine learning can deliver practical improvements in financial fraud detection performance for card-based and other payme…
Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
Hamed Gholipour, Farid Bozorgnia, Hamzeh Mohammadigheymasi +5
This paper develops a hybrid quantum approach for graph-based semi-supervised learning to enhance performance in scenarios where labeled data is scarce. We introduce two enhanced q…
Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery
Alejandro Giraldo, Daniel Ruiz, Mariano Caruso +2
Quantitative Structure-Activity Relationship (QSAR) modeling is a cornerstone of computational drug discovery. This research demonstrates the successful application of a Quantum Mu…
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms
Javier Mancilla, Christophe Pere
Quantum Machine Learning (QML) hasn't yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific c…