2 citations · 2 across the 2 of their papers we have counts for
2 papers
quant-ph2026
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…
q-fin.RM2024★ 2 cited
Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning
Javier Mancilla, André Sequeira, Tomas Tagliani +2
Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing inte…