3 papers
cs.LG2026
SAFE Quantum Machine Learning with Variational Quantum Classifiers
Ying Chen, Paolo Giudici, Vasily Kolesnikov +1
We propose a variational quantum classifier operating on high dimensional deep representations via amplitude encoding, stabilized by a learnable classical pre encoding layer.By com…
physics.comp-ph2026
Advancing Machine Learning Applications in Quantum Few-Body Systems
Jin Ziqi, Paolo Recchia, Mario Gattobigio
This paper presents a general neural network framework for solving quantum few-body systems, extending prior methods to handle diverse particle masses, interaction types, and syste…
q-fin.CP2026
Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
Ying Chen, Paul Griffin, Paolo Recchia +2
Recovery rate prediction plays a pivotal role in bond investment strategies by enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supportin…