Dresden 2026 – scientific programme
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QI: Fachverband Quanteninformation
QI 5: Quantum Computing and Algorithms II
QI 5.9: Talk
Tuesday, March 10, 2026, 12:00–12:15, BEY/0137
Identifying optimal non-classicality witnesses — •Martina Jung1, Suchitra Krishnaswamy2, Jan Sperling2, Timon Schapeler3, Tim Bartley3, Annabelle Bohrdt4, and Martin Gärttner1 — 1Institute of Condensed Matter Theory and Optics, Friedrich-Schiller-University, Jena, Germany — 2Institute for Photonic Quantum Systems, Theoretical Quantum Science, Paderborn University, Germany — 3Department of Physics, Paderborn University, Germany — 4Munich Center for Quantum Science and Technology (MCQST), München, Germany
Non-classicality, defined and understood in the quantum optical sense, acts as a resource for photon-based quantum technologies. Therefore, certifying the non-classicality of a quantum state is crucial to gauging its potential for quantum advantage. However, traditional non-classicality witnesses often fail in realistic scenarios involving finite-resolution photon detectors and limited statistics.
Here, we train a variational model using finitely many snapshots, measured with different detection schemes, to learn an optimal non-classicality witness for a given set of physically relevant states. The model is both device-agnostic and interpretable; the optimal witness can be extracted once the model has been trained. Training the model on experimental data measured with (i) a superconducting nanowire single-photon detector and (ii) a time-bin multiplexing detection scheme demonstrates the versatility of the approach, paving the way for efficient non-classicality certification in the lab.
Keywords: Machine Learning; Neural Network; Nonclassicality; Non-classicality; Photon detection