Quantum Information Cryptography: Adaptive Optimization of Entropy Gradient Algorithms

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Information Cryptography (QIC) relies heavily on the security of cryptographic keys. Traditional approaches often struggle with the dynamic nature of key generation and security, necessitating adaptive algorithms. This paper explores a novel optimization strategy for entropy gradient algorithms, specifically designed to address this challenge. We introduce a dynamically adaptive optimization algorithm that continuously adjusts the optimization objective based on real-time data distribution shifts. This contrasts with static parameter optimization, improving the algorithm's resilience to evolving data patterns. The core mechanism leverages a feedback-driven entropy gradient calculation to automatically fine-tune the objective function, enhancing the algorithm's adaptability and ultimately boosting the security of QIC systems. We present preliminary results demonstrating the effectiveness of this approach in a simulated environment, highlighting its potential for significant improvements in key generation and decryption performance.

DOI: 10.5281/zenodo.22219987

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