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quant-ph2025
Learning to stabilize nonequilibrium phases of matter with active feedback using partial information
Giovanni Cemin, Markus Schmitt, Marin Bukov
We investigate the role of information in active feedback control of quantum many-body systems using reinforcement learning. Active feedback breaks detailed balance, enabling the e…
quant-ph2024
Machine learning of quantum channels on NISQ devices
Giovanni Cemin, Marcel Cech, Erik Weiss +4
World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may st…
quant-ph2023
Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning
Giovanni Cemin, Francesco Carnazza, Sabine Andergassen +3
To characterize the dynamical behavior of many-body quantum systems, one is usually interested in the evolution of so-called order-parameters rather than in characterizing the full…