Adaptive Self-Organized Quantum Network Quantum Probability Distribution Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the development of an adaptive self-organized quantum network (ASQN) quantum probability distribution optimization algorithm. Traditional quantum computation relies heavily on human intervention in parameter tuning, leading to limitations in accuracy and efficiency. The proposed algorithm dynamically adjusts the probability distribution within the ASQN, automatically optimizing the quantum state for improved performance. The core mechanism involves a feedback loop utilizing system state to iteratively refine the probability distribution, offering a novel approach to quantum computing that surpasses existing optimization techniques. This work demonstrates the potential for significant improvements in quantum accuracy through automated adjustment, paving the way for more robust and reliable quantum computations.

DOI: 10.5281/zenodo.22246951

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