[qmeets talk] Machine learning for quantum data

Abstract: The efficient characterization of quantum states and processes through measurements is central to quantum technologies, be it to certify relevant resources like entanglement, or to extract relevant observables in quantum simulation experiments. The latter deal with complex quantum many-body systems, where the observed data is high-dimensional, making the extraction of quantities of interest notoriously challenging. We discuss possibilities for using modern machine learning methods, which specialize in analyzing large, high-dimensional datasets, to facilitate this task. For a recent review, see arXiv:2509.08011.

Speakers: Annabelle Bohrdt & Martin Gärttner

[Online seminar]
Format: Each talk will be 20 + 10 minutes, followed by 15 minutes of general discussion.

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