14 citations · 27 across the 7 of their papers we have counts for
8 papers · 1 filter
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…
Convergence monitoring of quantum Gibbs samplers
Nikolaos Louloudis, Ruben Ibarrondo, Mikel Sanz +1
Recent progress in fully quantum Markov chain Monte Carlo methods enables efficient Gibbs-state sampling on quantum computers [Chen et al., Nature 646, 561 (2025)]. Although rigoro…
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…
Contraction and Expansion Values of Quantum Channels
Ruben Ibarrondo, Mikel Sanz
The contraction coefficient of the trace distance is a central tool in quantum information, quantifying how strongly a quantum channel degrades the distinguishability of states. Ho…
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…
Quantum Active Learning
Yongcheng Ding, Yue Ban, Mikel Sanz +2
Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Trainin…
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…