A PAC-Bayesian approach to generalization for quantum models 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 capacity rather than the sp...

New #QML paper alert! 📝🧠⚛️

🧐 A PAC-Bayesian approach to generalization for quantum models
arxiv.org/abs/2603.22964

This collaboration involved some seriously hard and fun work by @pablones8.bsky.social, Matthias Caro, Carlos Bravo-Prieto, @fjschreiber.bsky.social, and @jenseisert.bsky.social

I really enjoyed giving this talk in Singapore!
Finally, in the coming weeks this project will see the light of day 🤞🏼

youtu.be/-J6AO0mq0_s

Quantum approximated cloning-assisted density matrix exponentiation, Pablo Rodriguez-Grasa, Ruben Ibarrondo, Javier Gonzalez-Conde, Yue Ban, Patrick Rebentrost, and Mikel Sanz @pablones8.bsky.social @conzavin.bsky.social
@qmisanz.bsky.social @nquirec.bsky.social #Quantum
Link below ⬇️

New paper published today in Phys. Rev. Research doi.org/10.1103/Phys... “Quantum approximated cloning-assisted density matrix exponentiation”. Congrats to @pablones8.bsky.social Rubén Ibarrondo, J. González-Conde, Y. Ban and P. Rebentrost! @upvehu.bsky.social #BCAM #Ikerbasque

New paper published today in Machine Learning: Science and Technology doi.org/10.1088/2632... on “Satellite image classification with neural quantum kernels”. Special congrats to @pablones8.bsky.social and to all collaborators! @upvehu.bsky.social