Wigner Function Reconstruction via Deep Generative Models

The Wigner function (WF) provides a phase-space representation of quantum states, with its negativity serving as a key indicator of nonclassicality. However, reconstructing WFs experimentally or numerically remains notoriously difficult due to their high dimensionality and intensive measurement requirements. In this work, we employ a ResNet-based deep generative model (DGM) to reconstruct WFs from a few accessible marginal distributions. Synthetic datasets are generated for three representative quantum-optical models—the single-atom Jaynes–Cummings model (JCM), the two-qubit Tavis–Cummings model (TJCM), and the pair-coherent state (PCS). The trained DGM faithfully reproduces characteristic quantum features—interference fringes, negativity, collapse–revival dynamics, and model-specific symmetries—with an average L2 error of below 0.15. These results confirm that deep generative learning can accurately infer joint quasi-distributions from sparse data while retaining physically meaningful correlations. Overall, this framework provides an efficient and scalable approach for quantum-state visualization, potentially complementing traditional quantum-state tomography in cavity and circuit QED systems.