Quantum Topology for Data Compression

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the potential of Quantum Topology as a novel data compression technique, predicated on the observation that certain data structures exhibit inherent topological properties. We investigate a quantum-based approach to encode data using quantum entanglement, aiming to achieve significantly higher compression ratios compared to existing classical methods. The core mechanism centers around exploiting these topological characteristics to represent information in a more compact and efficient manner. The paper presents a theoretical framework and outlines preliminary experimental validation demonstrating the potential of this approach.

DOI: 10.5281/zenodo.22320183

Related papers

Topological Information Encoding

Jincheng Zhang

This paper explores the application of topological information encoding as a novel approach to data compression and transmission, particularly within the context of complex quantum systems. Traditional data encoding methods often focus on representing data mathematically, while this work leverages the inherent properties of topological structures to achieve superior compression and transmission efficiency. We present a methodology that encodes quantum states directly into topological graphs, exploiting the graph's inherent structure to minimize redundancy and enhance data representation. The paper details the core mechanisms, the potential advantages of this approach, and preliminary results demonstrating its effectiveness in a simplified quantum context. The goal is to investigate a paradigm shift in data compression techniques, offering a more robust and potentially transformative solution for handling complex quantum data.

Sep 3, 20260 citations

Title: Topological Quantum Entanglement for Data Compression

Jincheng Zhang

This paper presents a novel approach to data compression utilizing topological quantum entanglement, aiming to surpass current classical methods through exponential compression rates. We propose a method for generating and manipulating hierarchical entanglement structures, representing data in a fundamentally different way than traditional methods. The core mechanism centers on building a layered, self-referential quantum network, enabling efficient encoding and retrieval of data through complex topological properties. The potential for significant compression gains, coupled with the inherent advantages of topological entanglement, makes this a promising research direction. The work explores the foundational principles, mathematical underpinnings, and experimental feasibility of this technique.

Aug 31, 20260 citations

Quantum-Enhanced Data Compression with Entangled State Encoding

Jincheng Zhang

This paper explores a novel approach to data compression leveraging the principles of quantum entanglement. Traditional classical compression algorithms often struggle to efficiently represent highly correlated data, particularly in complex datasets. This research proposes a method, termed entangled state encoding, where data is represented by the quantum state of an entangled pair. By exploiting the inherent correlations within the data, this technique aims to achieve significantly higher compression ratios compared to conventional methods. The core of the approach lies in encoding multiple data points simultaneously into the shared quantum state, effectively reducing redundancy. We analyze the theoretical limitations and potential advantages of this technique, focusing on the information-theoretic aspects of compression and the challenges associated with practical implementation. The study outlines the fundamental principles, provides a conceptual framework, and discusses the key considerations for future research in this emerging field. The central equation demonstrating the compression ratio is: R = (1 - P) * L, where R is the compression ratio, P is the probability of measurement error, and L is the original data length. Furthermore, we consider the entanglement fidelity (F) as a crucial parameter: F ∈ [0, 1], with higher fidelity leading to improved compression performance. The goal is to maximize the compression ratio while maintaining a high fidelity of the entangled state.

Aug 28, 20260 citations

拓扑空间数据压缩算法的量子化模拟

Jincheng Zhang

This paper investigates the application of quantum simulation techniques to data compression, specifically focusing on the unique properties of topological spaces. Traditional data compression methods often rely on classical simulation, which can be computationally expensive and limited in its ability to effectively handle the inherent structure of topological data. This research proposes a novel approach utilizing quantum simulation to compress data by modeling the underlying quantum states of the topological space. We demonstrate the potential of this method to achieve significantly higher compression rates compared to existing techniques, leveraging the inherent properties of topology for enhanced data representation. The core mechanism involves simulating the quantum evolution of data within a topologically constructed space, leading to a compression ratio significantly improved through quantum effects. We explore the fundamental principles of quantum simulation within the context of data compression and present preliminary results showcasing the effectiveness of this methodology.

Sep 7, 20260 citations

Quantum Entanglement Encoding for Multi-Dimensional Data Compression

Jincheng Zhang

This paper presents a novel approach to multi-dimensional data compression leveraging the unique properties of quantum entanglement. Traditional compression methods often struggle to overcome the inherent limitations imposed by the Shannon entropy theorem, particularly when dealing with complex, high-dimensional data. Our proposed method utilizes the correlated nature of entangled quantum states to achieve superior compression ratios. We demonstrate a mapping of multi-dimensional data into a set of entangled quantum states, exploiting the instantaneous correlations between these states to represent the data efficiently. The core of the method lies in manipulating the entangled states to encode information, potentially exceeding the compression limits achievable with classical techniques. The theoretical framework is outlined, focusing on the key mathematical principles underpinning the entanglement-based compression process. This work explores the potential of quantum mechanics to revolutionize data compression, offering a new paradigm for handling vast datasets with unprecedented efficiency. The method is presented with a focus on the mathematical formulation and the underlying principles.

Sep 3, 20260 citations

Quantum Entanglement Data Compression Algorithm

Jincheng Zhang

This paper introduces a novel data compression algorithm based on quantum entanglement, aiming to achieve superior compression efficiency compared to traditional methods. The core principle leverages the unique properties of quantum entanglement to achieve information redundancy and reduction, minimizing storage and transmission loss. We explore a mechanism that exploits the correlations inherent in entangled states to represent data more compactly. The algorithm's efficacy is demonstrated through theoretical analysis and preliminary experimental results.

Sep 3, 20260 citations