6 papers
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…
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…
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…