7 papers
Qutrit-Based Neural Quantum Kernels for Classification Tasks
Camila Cristiano-Romero, Pablo Rodriguez-Grasa, Mikel Sanz
Neural quantum kernels (NQKs) construct quantum kernels by pretraining a quantum neural network (QNN) and subsequently reusing the trained circuit as a task-adapted embedding. Exte…
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
Neutrino Telescope Event Classification on Quantum Computers
Pablo Rodriguez-Grasa, Pavel Zhelnin, Carlos A. Argüelles +1
Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum co…
Pulsed learning for quantum data re-uploading models
Ignacio B. Acedo, Pablo Rodriguez-Grasa, Pablo Garcia-Azorin +1
While Quantum Machine Learning (QML) holds great potential, its practical realization on Noisy Intermediate-Scale Quantum (NISQ) hardware has been hindered by the limitations of va…
Quantum approximated cloning-assisted density matrix exponentiation
Pablo Rodriguez-Grasa, Ruben Ibarrondo, Javier Gonzalez-Conde +3
Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the e…
Neural quantum kernels: training quantum kernels with quantum neural networks
Pablo Rodriguez-Grasa, Yue Ban, Mikel Sanz
Quantum and classical machine learning have been naturally connected through kernel methods, which have also served as proof-of-concept for quantum advantage. Quantum embeddings en…