Quantum Information Entropy: Dynamic Computation and Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum information entropy, a fundamental measure of uncertainty, is a cornerstone of quantum mechanics and a critical factor in the design of quantum algorithms and systems. This paper investigates the application of dynamic computation techniques, leveraging quantum information entropy, to optimize quantum system performance and efficiency. We propose a novel algorithm that dynamically calculates and optimizes quantum systems based on the inherent properties of this entropy, aiming to maximize information utilization and improve overall system functionality. The core mechanism centers around a quantum-state evolution algorithm that adapts to the system's state, dynamically adjusting parameters to achieve optimal performance. This work introduces a new approach to quantum system optimization, demonstrating potential improvements in key metrics including fidelity and reduced error rates.

DOI: 10.5281/zenodo.22327824

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