量化时间因果关系建模算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22189630

Related papers

基于量子信息的优化算法的非线性时间序列分析

Jincheng Zhang

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.

Sep 4, 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

基于量子计算的概率推理算法

Jincheng Zhang

This paper presents a novel probabilistic reasoning algorithm based on quantum computing, aiming to address limitations of traditional algorithms in handling complex probabilistic scenarios. The core of this approach leverages quantum properties, specifically superposition and entanglement, to drastically accelerate the computation of probability distributions and inference. We demonstrate the algorithm's effectiveness through theoretical analysis and preliminary experimental validation, showcasing significant improvements in efficiency and accuracy compared to existing methods for specific probabilistic problems. The proposed algorithm's unique mechanism focuses on leveraging quantum entanglement for enhanced probability state manipulation, ultimately offering a paradigm shift in probabilistic computation.

Aug 29, 20260 citations

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

基于量子模型的概率推理

Jincheng Zhang

This paper investigates the potential of quantum computing to revolutionize probabilistic reasoning, focusing on the development of a novel system for complex model inference and prediction. Traditional probabilistic reasoning methods are limited by computational constraints, while quantum computing offers the promise of enhanced speed and accuracy. We propose a system that leverages quantum algorithms to simulate probabilistic behavior and guide prediction, with a specific emphasis on applications in finance and bioinformatics. This work explores the core mechanism and potential benefits of utilizing quantum computation for probabilistic inference. The paper outlines the key innovations and challenges associated with this approach, ultimately aiming to demonstrate the feasibility of quantum-enhanced probabilistic reasoning.

Sep 2, 20260 citations

基于量子隐描述的复杂系统建模和仿真

Jincheng Zhang

This paper investigates the application of quantum entanglement and quantum computation to model and simulate complex systems. Traditional numerical methods often struggle with the inherent complexity of these systems, limiting their applicability. We propose a novel approach utilizing quantum entanglement to create a quantum representation of the system's state, enabling efficient simulation and enhanced modeling capabilities. This research focuses on building a robust and scalable model for a specific, complex system – the dynamics of a fluid flow involving intricate interactions and emergent behavior. The core mechanism involves encoding the system's variables using quantum entanglement, leveraging the unique properties of these systems to accelerate simulation and achieve a high degree of fidelity. This work demonstrates the potential of quantum entanglement to revolutionize the modeling and simulation of complex systems, offering a pathway towards more accurate and efficient analysis.

Sep 5, 20260 citations