自适应量子隐形矩阵的自适应优化算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive quantum hidden Markov model (QHM) optimization algorithm designed to enhance computational efficiency and accuracy, particularly for matrices exhibiting high complexity. Traditional QHM methods rely on static parameter adjustments, while our approach dynamically adjusts quantum matrix parameters to optimize the calculation process. We explore a new structure for the quantum matrix, incorporating a self-tuning mechanism to mitigate computational bottlenecks. The algorithm's effectiveness is validated through simulations, demonstrating improved performance metrics compared to existing approaches, especially in scenarios with significant matrix dimensions. This work offers a significant step toward robust and adaptive quantum computing, addressing a critical challenge in the field.

DOI: 10.5281/zenodo.22318771

Related papers

基于量子自适应的动态隐马赛克算法优化

Jincheng Zhang

This paper investigates the application of a novel quantum-adaptive dynamic hidden Markov model (DHMM) optimization algorithm designed to enhance the performance of various machine learning tasks, particularly those involving complex datasets. Traditional DHMM approaches often suffer from suboptimal parameter adjustment, limiting their effectiveness. We propose a quantum-driven dynamic adjustment of the DHMM's parameter space, allowing for automated optimization based on the input data's unique characteristics. This approach leverages quantum self-stabilization to iteratively refine the model's state, leading to improved accuracy, faster convergence, and enhanced robustness. The study demonstrates significant improvements in performance across a range of datasets, including image classification and natural language processing, validating the efficacy of this quantum-adaptive strategy.

Sep 2, 20260 citations

基于量子自适应的计算图优化

Jincheng Zhang

This paper introduces a novel computational graph optimization technique leveraging quantum self-adaptivity. Traditional graph optimization methods often rely on static parameter settings, which struggle to effectively address the dynamic nature of complex systems. We propose a quantum self-adaptative algorithm that dynamically adjusts quantum state parameters to enhance both computational efficiency and accuracy. The core mechanism involves constructing a dynamic graph structure and employing an iterative adjustment process based on quantum state transformations. This approach offers a significant advancement over existing methods by providing a more flexible and adaptive optimization strategy, particularly suitable for systems exhibiting complex dynamic behavior. The proposed method demonstrates substantial improvements in various graph optimization tasks, highlighting its potential for revolutionizing the field of computational graph design.

Sep 7, 20260 citations

Self-Adaptive Quantum Hiding Matrix Optimization Algorithm

Jincheng Zhang

This paper introduces a novel optimization algorithm based on self-adaptive quantum hiding matrices. The algorithm leverages the unique properties of quantum hiding matrices to automatically adjust their parameters, enabling high-precision and efficient quantum computation. Traditional quantum algorithms often rely on manual parameter tuning, which can be inefficient and limit algorithm scalability. This proposed algorithm offers a significant advancement by incorporating dynamic parameter adjustment, directly addressing these limitations. We present a theoretical framework and experimental validation demonstrating the algorithm's effectiveness in optimizing quantum circuits and achieving superior performance compared to existing methods. The core mechanism centers around the algorithm's ability to dynamically modify the hiding matrix based on the quantum state, facilitating optimal parameter adjustments.

Aug 31, 20260 citations

基于自适应的量子机器学习的概率模型优化

Jincheng Zhang

Quantum Machine Learning (QML) offers the potential for significant advancements in computational capabilities, particularly in areas requiring complex data analysis and pattern recognition. However, the inherent complexity of quantum algorithms often necessitates careful parameter optimization, a process traditionally reliant on human intervention. This paper presents a novel approach to quantum machine learning – a probabilistic model optimization algorithm based on adaptive parameter adjustment – designed to dramatically improve training efficiency and model performance. We introduce a method that dynamically adjusts the parameters of a probabilistic model, leveraging self-tuning capabilities to achieve a more robust and efficient training cycle. This represents a significant step forward, addressing a critical bottleneck in the development of quantum machine learning models.

Sep 2, 20260 citations

基于自适应的量子算法优化策略

Jincheng Zhang

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.

Sep 6, 20260 citations

基于量子自适应的概率模型优化

Jincheng Zhang

This paper introduces a novel quantum self-adaptive probability model optimization algorithm. Traditional probabilistic models often rely on manual parameter adjustment, limiting the algorithm's effectiveness. This work leverages the unique properties of quantum entanglement and quantum computing to achieve a more robust and adaptable optimization process. We propose a quantum-enhanced optimization strategy that dynamically adjusts the model parameters based on quantum correlations, resulting in improved model reliability and generalization capabilities. The algorithm's core mechanism centers around establishing and manipulating quantum entanglement between model parameters, facilitating a more efficient and precise parameter search. This approach offers a promising alternative to classical optimization methods, particularly for complex probabilistic models requiring high accuracy and robustness. The results demonstrate the algorithm's effectiveness through a series of simulations and comparisons with established optimization techniques, showcasing its potential for significant advancements in probabilistic modeling.

Sep 5, 20260 citations