Quantum Data Compression Based on Quantum Demixing

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores a novel approach to classical data compression leveraging the principles of quantum demixing and measurement. The core idea is to transform classical data into a quantum state representation and subsequently exploit quantum demixing to effectively eliminate unwanted quantum correlations. This process allows for the selective extraction of information, ultimately leading to a compressed representation. We present a theoretical framework outlining this methodology, focusing on the mathematical underpinnings of the transformation, demixing, and measurement stages. The proposed method offers a potential pathway to achieve high compression ratios by exploiting the unique properties of quantum systems. We demonstrate, through theoretical analysis, the feasibility and potential advantages of this technique compared to conventional classical compression methods. The key elements involve mapping classical data to a quantum state, employing a demixing protocol to reduce redundancy, and finally, measuring the quantum state to recover the compressed information. This work lays the groundwork for future research into practical implementations of quantum data compression. ---

DOI: 10.5281/zenodo.22243987

Related papers

Based on Quantum Entanglement: Non-Local Data Compression

Jincheng Zhang

This paper explores a novel approach to data compression leveraging the principles of quantum entanglement. Traditional data compression methods rely on identifying and eliminating redundancy within data, often achieving limitations in compression ratios. We propose a system that utilizes entangled quantum states to represent data, achieving potentially higher compression rates and, crucially, lossless reconstruction. The core idea involves decomposing data into a set of entangled quantum subsystems. By exploiting the inherent correlations present within entangled states, we can effectively reduce redundancy without losing information. The paper details the mathematical framework underlying this approach, focusing on the key concepts of entanglement, quantum measurement, and the reconstruction process. We outline the theoretical advantages of this method compared to conventional techniques and discuss the challenges associated with its practical implementation. The primary goal is to demonstrate the potential of quantum entanglement for fundamentally transforming data compression, moving beyond the constraints of classical redundancy reduction. ---

Aug 31, 20260 citations

Quantum Data Compression Based on Quantum State Reordering

Jincheng Zhang

This paper explores a novel approach to quantum data compression utilizing the principles of quantum state reordering. Traditional quantum data compression techniques often suffer from inefficiencies, primarily due to the inherent complexities of representing and manipulating quantum information. This work proposes a method that leverages quantum state reordering to achieve more effective data reduction. The core idea involves encoding quantum data into specific quantum states and subsequently applying reordering algorithms designed to exploit redundancies within the encoded states. This approach aligns with fundamental quantum information theory and provides a potentially more efficient pathway to compressing quantum data, reducing storage and transmission requirements. The theoretical framework presented outlines the key components, including state encoding, reordering algorithm design, and compression performance analysis. The ultimate goal is to demonstrate the feasibility and potential benefits of this technique for future quantum data management systems.

Aug 28, 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

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 State Reorganization and Information Compression

Jincheng Zhang

This paper presents a novel approach to information compression leveraging quantum state reorganization. The core idea is to exploit the inherent correlations within quantum states to achieve compression rates surpassing those attainable by conventional classical methods. This method utilizes quantum entanglement and quantum state reorganization techniques to encode information across multiple qubits. By intelligently manipulating these quantum correlations, we demonstrate a potential for significantly enhanced compression efficiency. The theoretical framework is presented, outlining the key principles and mathematical formulations underpinning this approach. We introduce a new algorithm based on iterative state reorganization, aiming to minimize the quantum state representation while preserving the maximum amount of information. The success of this method hinges on the ability to identify and exploit redundant or correlated quantum states, a process we formalize with mathematical models. The presented framework offers a promising direction for future research in quantum information processing and compression.

Sep 5, 20260 citations

Quantum-Inspired Entanglement-Based Data Compression

Jincheng Zhang

This paper investigates a novel approach to data compression inspired by the principles of quantum entanglement. Traditional data compression techniques often struggle to efficiently represent complex data patterns, particularly in scenarios with high redundancy. We propose an algorithm that utilizes the concept of entangled qubits to represent data, exploiting the strong correlations between these qubits to achieve potentially higher compression ratios. The core idea involves encoding data into a set of entangled qubits, where the state of one qubit directly influences the state of the others. This inherently creates a compact representation, reducing the overall data size. The algorithm is designed to be computationally efficient, aiming to mimic the core advantages of quantum mechanics without requiring full quantum computing resources. We explore the theoretical foundations of this approach, detailing the mathematical framework for qubit representation and the encoding/decoding processes. The paper highlights the potential advantages and challenges associated with this quantum-inspired method, suggesting future research directions and refinements.

Aug 31, 20260 citations