Interpretable, machine learning-based model learns to recognize nonclassical states
New preprint by Martina Jung with collaborators from Paderborn and Munich
Model architecture for classifying quantum states as classical or nonclassical based on photon-number measurements
Published:
Learning to detect optical nonclassicality
Nonclassicality, defined in the quantum optical sense, serves as a resource for photonic quantum technologies. Therefore, certifying the nonclassicality of a quantum state is crucial for gauging its potential for quantum advantage. In this work, we train a variational model can be trained to distinguish classical from nonclassical states using measurement samples from photon-number-resolving detection schemes. Hereby, we exploit prior knowledge about the class of states that are likely to occur in a specific experiment to reduce the required amount of experimental data that needs to be gathered to reliably detect nonclassical states.
Link to paper: https://arxiv.org/abs/2603.06319External link