39 citations · 64 across the 8 of their papers we have counts for
10 papers
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz +1
In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to…
Quantum Hamiltonian Evolution for Coherent Quantum Learning
Ignacio B. Acedo, Javier Gonzalez-Conde, Pablo Rodriguez-Grasa +2
We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamilton…
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…
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…
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…
Satellite image classification with neural quantum kernels
Pablo Rodriguez-Grasa, Robert Farzan-Rodriguez, Gabriele Novelli +2
Achieving practical applications of quantum machine learning for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel appr…
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…
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…
Efficient quantum amplitude encoding of polynomial functions
Javier Gonzalez-Conde, Thomas W. Watts, Pablo Rodriguez-Grasa +1
Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficie…