3 papers
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-ph2024
Performance Bounds for Quantum Feedback Control
Flemming Holtorf, Frank Schäfer, Julian Arnold +2
The limits of quantum feedback control have immediate consequences for quantum information science at large, yet remain largely unexplored. Here, we combine quantum filtering theor…
cond-mat.dis-nn2024
Machine learning the Ising transition: A comparison between discriminative and generative approaches
Difei Zhang, Frank Schäfer, Julian Arnold
The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems ca…