2025/12/21 by Xiaoting Gao, Yan Zhu, Gao, Xiaoting +7
Computer Science · Physics and Astronomy · #Quantum Information and Cryptography #Quantum Computing Algorithms and Architecture #Cold Atom Physics and Bose-Einstein Condensates
paper · doi:10.48550/arxiv.2512.18801
Machine learning methods have been used to infer specific properties of limited families of optical quantum states, but a unified model that predicts a broad range of properties for practically relevant-especially multimode non-Gaussian-states without full tomography is still lacking. Here we introduce the first foundation model for the characterization of optical quantum states across a wide range of complexity, defined by three key factors: non-Gaussianity, number of modes, and degree of squeezing. We show that a single model pretrained on low-complexity states can be directly applied to characterize states of higher complexity. With limited fine-tuning, the model adapts to downstream tasks such as predicting quantum fidelity and Wigner negativity over a broad class of experimentally relevant states, including strongly non-Gaussian Schrödinger cat states, multimode systems with up to ten modes, and highly squeezed states with squeezing levels up to 10.4dB. Our results establish a unified framework for characterizing optical quantum states from limited measurement data, enabling efficient certification of quantum states relevant to optical quantum information computation, communication and metrology.