{"ID":2888453,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23695","arxiv_id":"2507.23695","title":"On the Achievable Rate of Satellite Quantum Communication Channel using Deep Autoencoder Gaussian Mixture Model","abstract":"We present a comparative study of the Gaussian mixture model (GMM) and the Deep Autoencoder Gaussian Mixture Model (DAGMM) for estimating satellite quantum channel capacity, considering hybrid quantum noise (HQN) and transmission constraints. While GMM is simple and interpretable, DAGMM better captures non-linear variations and noise distributions. Simulations show that DAGMM provides tighter capacity bounds and improved clustering. This introduces the Deep Cluster Gaussian Mixture Model (DCGMM) for high-dimensional quantum data analysis in quantum satellite communication.","short_abstract":"We present a comparative study of the Gaussian mixture model (GMM) and the Deep Autoencoder Gaussian Mixture Model (DAGMM) for estimating satellite quantum channel capacity, considering hybrid quantum noise (HQN) and transmission constraints. While GMM is simple and interpretable, DAGMM better captures non-linear varia...","url_abs":"https://arxiv.org/abs/2507.23695","url_pdf":"https://arxiv.org/pdf/2507.23695v1","authors":"[\"Mouli Chakraborty\",\"Subhash Chandra\",\"Avishek Nag\",\"Anshu Mukherjee\"]","published":"2025-07-31T16:13:42Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}