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Quantum Entanglement Encoding for Adaptive Data Compression

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

This paper investigates a novel approach to data compression leveraging the unique properties of quantum entanglement. The core idea is to employ an adaptive entanglement encoding scheme, where the encoding strategy is dynamically adjusted based on the statistical characteristics of the input data and the entanglement state itself. This contrasts with traditional static entanglement encoding methods which often lack the adaptability required to mitigate noise and maximize information density. The proposed system constructs an encoder-decoder framework utilizing entangled qubits. The encoder maps information onto these qubits, and the decoder reconstructs the original data through measurement. The adaptive component intelligently modifies the entanglement parameters – such as entanglement degree and qubit subsets – during encoding, optimizing for both information capacity and robustness against noise. We demonstrate, theoretically, that this adaptive approach can surpass the classical data compression limits, offering a significant advantage in scenarios where efficient data representation is paramount. The key contribution lies in the dynamic control of entanglement, enabling a more nuanced and effective utilization of quantum correlations. The theoretical analysis provides a framework for designing and implementing robust quantum compression systems. ---

DOI
10.5281/zenodo.22198111

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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.