基于量子信息的优化算法的非线性时间序列分析

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm leveraging quantum information principles to efficiently analyze non-linear time series data. Traditional approaches often struggle with the inherent complexity of these datasets, demanding significant computational resources. We propose a quantum-enhanced algorithm that utilizes quantum state manipulation – specifically superposition and entanglement – to accelerate the analysis and prediction process. This paper details the core mechanism, including the quantum encoding of data and the subsequent optimization process. The algorithm demonstrates significant improvements in speed and accuracy compared to classical methods, specifically when dealing with complex time series patterns. The research explores the potential of quantum computing to revolutionize time series analysis, offering a promising pathway for enhanced data processing and forecasting.

DOI: 10.5281/zenodo.22289989

Related papers

Quantum-Based Non-linear Time Series Analysis

Jincheng Zhang

This paper introduces a novel approach to non-linear time series analysis utilizing the unique capabilities of quantum computing. Traditional methods often struggle with the complexity of such sequences, limiting their analytical and modeling potential. We propose a quantum-enhanced algorithm that leverages quantum superposition and entanglement to achieve more accurate and efficient analysis, addressing limitations inherent in classical approaches. This work explores the potential of quantum computation to transform the analysis of non-linear time series data, offering a promising avenue for advanced modeling and forecasting. The core mechanism involves a quantum-enhanced Fourier transform coupled with a tailored optimization algorithm. The paper details the algorithm's design, implementation, and preliminary results demonstrating improved accuracy and efficiency compared to established classical methods. Finally, we discuss the challenges and future directions for this technology.

Sep 4, 20260 citations

Quantum Entanglement-Based Information Encoding and Time Series Prediction

Jincheng Zhang

This paper investigates the potential of utilizing quantum entanglement for enhancing time series prediction. Traditional time series forecasting methods often struggle with complex, non-linear patterns and high-dimensional data. We propose a novel approach that leverages the unique properties of quantum entanglement to encode time series information and extract predictive insights. Specifically, we map time series data onto entangled quantum states, allowing us to access correlations and patterns that may be missed by classical methods. The theoretical framework outlines the key principles, including the mapping process, the measurement strategy, and the expected improvement in predictive accuracy. We present a mathematical model incorporating entanglement entropy and its influence on the prediction process. The core claim is that this entanglement-based encoding significantly improves time series prediction. The research highlights a fundamentally different approach to time series analysis, moving beyond classical information processing to exploit the quantum mechanical phenomenon of entanglement.

Aug 31, 20260 citations

量化时间因果关系建模算法

Jincheng Zhang

This paper introduces a novel quantum computing algorithm for the efficient modeling and inference of time-dependent causal relationships. Traditional probabilistic and statistical models struggle to effectively handle complex time-series data, particularly in simulating and predicting intricate systems. We propose a quantum-enhanced model that leverages quantum entanglement and quantum state manipulation to achieve a higher degree of precision in capturing causal dependencies. The core mechanism centers around constructing a quantum causal model, utilizing quantum algorithms for computational reasoning and ultimately, providing a more accurate representation of time- causality. This research explores the potential of quantum computing to revolutionize time-series analysis and forecasting, offering a significant advancement over existing approaches.

Aug 31, 20260 citations

Title: Quantum-Enhanced Topological Data Analysis for Time Series Prediction

Jincheng Zhang

Quantum-enhanced topological data analysis (TDA) offers a novel approach to time series prediction, leveraging the unique properties of quantum mechanics. This paper proposes a new quantum algorithm designed to enhance the accuracy of predictive modeling by simultaneously exploring multiple temporal patterns. We explore the potential of quantum superposition and entanglement to identify subtle correlations that are often missed by classical methods. The core mechanism involves utilizing quantum algorithms to represent and process time series data in a fundamentally different manner, potentially leading to improved predictive capabilities. We detail the algorithm's design, its mathematical underpinnings, and preliminary experimental results demonstrating enhanced accuracy in a simulated time series dataset. The core claim centers on the potential for quantum algorithms to surpass classical methods in identifying complex, non-linear patterns inherent in time series data.

Aug 31, 20260 citations

基于量子纠缠的多变量时间序列预测

Jincheng Zhang

This paper explores the potential of leveraging quantum entanglement for improved multi-variable time series forecasting. Traditional time series methods often struggle with high dimensionality, nonlinearity, and complex dependencies within time series data. We propose a novel approach that maps time series data into a quantum state space, utilizing entangled qubits to capture intricate correlations. Through controlled manipulation of these entangled states and subsequent quantum measurements, we aim to extract predictive information. This research investigates the core claim that quantum entanglement can significantly enhance forecasting accuracy and robustness, particularly in scenarios with high dimensionality and complex relationships. The core mechanism involves mapping time series data to a quantum state space and utilizing entanglement to capture complex dependencies. The resulting predictions are obtained through quantum measurement and post-processing. This work offers a new paradigm for time series forecasting by harnessing the unique properties of quantum mechanics.

Sep 6, 20260 citations

Quantum Computing Distributed Time Sequence Prediction

Jincheng Zhang

This paper investigates the application of quantum computing to enhance time series prediction, specifically focusing on the processing of long time series data. Traditional methods often struggle with the computational complexity associated with these datasets, limiting their predictive capabilities. We propose a distributed time series prediction system leveraging quantum algorithms and quantum phenomena to significantly improve prediction accuracy, particularly when dealing with extended temporal patterns. This work explores the potential of quantum acceleration in handling long-range dependencies inherent in time series data.

Sep 5, 20260 citations