Quantum Adaptive Probability Distribution Optimization Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum-enhanced probability distribution optimization algorithm, termed Quantum Adaptive Probability Distribution Optimization Algorithm (QADPOA). Traditional methods often require extensive manual parameter adjustment, limiting the adaptability of models to complex data and patterns. QADPOA leverages quantum computing simulations to automatically adjust probability distribution parameters, thereby improving model robustness and predictive capabilities. The algorithm's core mechanism involves quantum simulation of probability distributions, followed by parameter optimization using quantum annealing techniques. This approach offers a paradigm shift in probabilistic modeling, potentially surpassing the limitations of classical algorithms. We demonstrate the algorithm's effectiveness through a series of illustrative examples, showcasing its ability to accurately model and predict complex data distributions.

DOI: 10.5281/zenodo.22454298

Related papers

基于量子自适应的概率分布优化

Jincheng Zhang

This paper presents a novel probabilistic distribution optimization algorithm based on quantum self-adaptation. Traditional methods rely on manual parameter adjustments, while quantum self-adaptation leverages quantum entanglement and quantum computation to dynamically optimize probability distributions, leading to enhanced prediction accuracy. The core mechanism involves manipulating a quantum system through quantum entanglement and quantum computation to adjust key parameters, ultimately improving the accuracy of probabilistic models. This work demonstrates the potential of this approach to outperform traditional optimization techniques in complex scenarios, offering a promising pathway toward more robust and efficient probabilistic modeling.

Sep 5, 20260 citations

基于量子态的动态概率分布优化算法

Jincheng Zhang

This paper presents a novel dynamic probability distribution optimization algorithm based on quantum state evolution. Traditional probabilistic distribution optimization methods often rely on static parameter adjustments, limiting their effectiveness and generalization capabilities. We propose an algorithm that leverages the inherent quantum properties of probability distributions – specifically, their ability to adapt and evolve – to dynamically adjust the distribution parameters during the optimization process. The algorithm's core mechanism involves a quantum state representation, a feedback loop based on the state's evolution, and a controlled parameter update strategy. We demonstrate the algorithm's superiority through theoretical analysis and extensive numerical experiments, showcasing significant improvements in optimization performance and robustness compared to existing methods. The core claim is that the algorithm's quantum state adaptation allows for more efficient and reliable optimization compared to traditional approaches.

Sep 7, 20260 citations

Quantum Adaptive Probability Model

Jincheng Zhang

This paper introduces a novel probabilistic model based on quantum adaptation, designed to efficiently model and predict complex systems. The core mechanism leverages quantum self-adaptive algorithms to dynamically adjust probability model parameters, resulting in enhanced prediction accuracy. Traditional probabilistic models suffer from limitations in adaptability, necessitating manual parameter tuning. This paper demonstrates the effectiveness of quantum self-adaptation in significantly improving predictive capabilities across a range of complex systems. This research utilizes a quantum self-adaptive algorithm to fine-tune probability distribution, thereby enhancing the model's accuracy.

Aug 31, 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

基于量子自适应的概率模型优化算法

Jincheng Zhang

This paper introduces a novel probabilistic model optimization algorithm based on quantum self-adaptation. Traditional optimization methods often rely on manual parameter adjustments, lacking adaptability. We propose a new algorithm that leverages the unique properties of quantum probabilistic models to dynamically adjust model parameters, resulting in significantly more accurate probability predictions. The core mechanism centers around a quantum probabilistic model framework, enabling automated parameter adjustment and improved predictive accuracy. This algorithm offers a promising advancement in probabilistic modeling and optimization, particularly for complex systems with high-dimensional data.

Sep 5, 20260 citations

Quantum Adaptive Machine Learning Framework

Jincheng Zhang

This paper introduces a novel quantum machine learning framework, dubbed "Quantum Adaptive Machine Learning," designed to dynamically adjust model parameters based on the input data's probabilistic distribution. Traditional machine learning approaches often struggle with data distribution shifts, necessitating manual parameter tuning. Our framework leverages adaptive adjustment, mimicking data distribution to optimize model performance. The core mechanism involves simulating data distributions and employing machine learning to enhance model adaptability. This results in a more robust and responsive learning process, particularly effective when data characteristics evolve over time. The research demonstrates the framework's ability to achieve superior accuracy and generalization compared to traditional methods in a series of benchmark datasets.

Aug 31, 20260 citations