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quant-ph2025
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
quant-ph2025
Tunable two-species spin models with Rydberg atoms in circular and elliptical states
Jacek Dobrzyniecki, Paula Heim, MichaÅ Tomza
We propose a scheme for constructing versatile quantum simulators using ultracold Rydberg atoms in long-lived circular and elliptical states. By exciting different subspaces of int…
quant-ph2024
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
Kacper CybiÅski, Marcin PÅodzieÅ, MichaÅ Tomza +3
Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of…