Harnessing Quantum Superalgebras for AI-Optimized Quantum Error Correction

Abstract: Quantum computing is on the cusp of revolutionizing computation, but its potential is hindered by the fragility of qubits and the persistent issue of quantum errors. This paper explores a novel approach to quantum error correction (QEC) by integrating quantum superalgebras, mathematical structures that extend Lie algebras with supersymmetry, into AI-driven optimization frameworks. By leveraging the symmetry-preserving properties of superalgebras and combining them with machine learning models, we propose a robust, scalable, and adaptive QEC system. This fusion enables real-time error diagnosis and correction, enhances quantum coherence, and paves the way for fault-tolerant quantum systems. Keywords: Quantum Superalgebras, Quantum Error Correction, Artificial Intelligence, Fault-Tolerant Quantum Computing, Lie Algebras, Quantum Machine Learning, Qubits, Quantum Channels