Title: Quantum Adaptive Probability System Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum adaptive probability system modeling aims to develop a novel approach to modeling quantum systems by leveraging quantum self-adaptability. This research explores the application of quantum self-adaptability to enhance system stability and accelerate quantum computation. We propose a framework that dynamically adjusts system parameters based on internal quantum states, thereby mitigating inherent instability issues commonly encountered in quantum systems. This model will provide a significant advancement in the design and management of quantum algorithms, potentially offering improved performance and robustness. The core mechanism lies in the implementation of a self-adjusting parameter adjustment process triggered by quantum state fluctuations. This approach contrasts with traditional, static models, which lack the ability to adapt to changing conditions. The paper will detail the design, implementation, and testing of this quantum adaptive probability system model, showcasing its potential to improve the stability and efficiency of quantum computations. The investigation will focus on demonstrating the efficacy of this model through simulations and, ultimately, potentially, preliminary experimental validation of its capabilities.

DOI: 10.5281/zenodo.22205629

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