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Redes neuronales gráficas bipartitas recurrentes para la decodificación de errores QC-MDPC en criptosistemas McEliece

arXiv:2610.38479v1 Anuncio Tipo: nuevo Resumen: A medida que la llegada de la computación cuántica a gran escala amenaza el cifrado clásico, los códigos de verificación de paridad de densidad moderada cuasicíclica (QC-MDPC) han surgido como un elemento fundamental

La noticia

arXiv:2610.38479v1 Announce Type: new Abstract: As the advent of large-scale quantum computing threatens classical encryption, Quasi-Cyclic Moderate Density Parity-Check (QC-MDPC) codes have emerged as a foundational element of Post-Quantum Cryptography (PQC). However, decoding these relatively dense codes using classical Belief Propagation algorithms often leads to failures caused by short graph cycles that stall the decoder. In cryptographic applications, these failures are severe security vulnerabilities that leak secret key information. To address this, this paper proposes a Recurrent Bipartite Graph Neural Network (R-BGNN) as proof of concept. By integrating Gated Recurrent Units (GRUs) into the message-passing framework and employing a multi-topology curriculum training strategy, the R-BGNN actively learns to break limit-cycle