Training-efficient density quantum machine learning

Abstract Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural networks, a model family that prepares mixtures of trainable unitaries, with a distributional constraint over coe...

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Main Authors: Brian Coyle, Snehal Raj, Natansh Mathur, El Amine Cherrat, Nishant Jain, Skander Kazdaghli, Iordanis Kerenidis
Format: Artigo
Sprog:Inglês
Udgivet: Nature Portfolio 2025-11-01
Serier:npj Quantum Information
Online adgang:https://doi.org/10.1038/s41534-025-01099-6
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