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Core Claim: A novel quantum algorithm optimization framework utilizing a high-Gaussian function adaptation strategy to automatically adjust algorithm parameters and improve efficiency, minimizing error propagation.

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

Quantum computation offers tremendous potential for scientific discovery and technological advancement, but its practical application is often limited by computational complexity and error rates. This research explores a new approach to quantum algorithm optimization, leveraging the principles of Gaussian function adaptation. The proposed framework dynamically adjusts quantum algorithm parameters based on the algorithm's specific characteristics, thereby reducing computational error and enhancing efficiency. This is a significant departure from traditional optimization methods that rely on manual parameter tuning, enabling a more adaptive and robust optimization process. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing substantial improvements in algorithm performance and reduced error metrics compared to baseline algorithms. The core mechanism lies in the continuous adjustment of algorithm parameters using Gaussian function transformations, creating a self-learning optimization process. This paper presents a comprehensive analysis and implementation of this framework, offering a promising pathway towards more efficient and reliable quantum computations.

DOI
10.5281/zenodo.22270548

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This paper proposes a novel quantum algorithm optimization strategy that leverages adaptive learning algorithms to dynamically adjust quantum algorithm parameters. Traditional approaches often rely on manual parameter tuning, which can be time-consuming and suboptimal. Our method aims to improve quantum algorithm efficiency and accuracy by automatically optimizing these parameters based on the input data. We present a framework that incorporates a self-learning mechanism to adaptively adjust parameters, offering a significant improvement over existing methods. The core of our approach involves a dynamic parameter adjustment process guided by a learned model that constantly evaluates the algorithm's performance. We demonstrate the effectiveness of this strategy through theoretical analysis and simulations, showcasing its potential to accelerate algorithm execution and enhance results.
This paper explores a novel optimization strategy for quantum algorithms, focusing on dynamic parameter adjustment to enhance efficiency and accuracy. Traditional algorithmic optimization methods often rely on static parameters, which struggle to adapt to the complexities of various quantum algorithms. This research introduces a self-adaptive framework that dynamically adjusts algorithm parameters, enabling precise and improved performance. We detail the core mechanism, propose a robust optimization process, and provide a preliminary assessment of its effectiveness. The goal is to establish a practical and adaptable approach for quantum algorithm development and optimization.
This paper introduces a novel optimization algorithm framework leveraging quantum self-adaptation. Traditional optimization methods rely on manual parameter tuning, lacking inherent adaptability. Our approach utilizes quantum self-adaptation, introducing the concept of "quantum optimization" to dynamically adjust optimization parameters and enhance algorithmic efficiency. We demonstrate the effectiveness of this framework through a series of simulations, showcasing improved performance on a benchmark set of optimization problems. The core mechanism involves employing quantum-enhanced gradient estimation and adaptive parameter adjustment, resulting in a more robust and efficient optimization process. The paper concludes with a discussion of the potential implications and future research directions of this innovative approach.
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