Quantum-Inspired Loss Functions for Artificial Intelligence Optimization: In the EQST-GP Framework from Fundamental Physics
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This paper introduces a novel framework for quantum-inspired optimization in artificial intelligence, derived directly from the fundamental principles of the Expanded Quantum String Theory with Gluonic Plasma (EQST-GP). We demonstrate how the mathematical structure of unified physics naturally gives rise to powerful optimization algorithms and loss functions that transcend conventional approaches. By mapping physical principles—such as gauge invariance, topological stability, and dynamic screening—to machine learning paradigms, we develop optimization techniques with provable convergence guarantees, enhanced exploration capabilities, and inherent regularization properties. The resulting framework achieves state-of-the-art performance across diverse optimization domains while maintaining mathematical elegance and physical interpretability. This work establishes a deep connection between fundamental physics and artificial intelligence, opening new avenues for both theoretical development and practical applications.
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