Quantum Information Compression

Yuxiang Yang

Jincheng Zhang

Quantum information compression is a critical field for preserving and transmitting quantum information, offering the potential to significantly reduce the volume of data required. Conventional compression algorithms often rely on static parameters, limiting the effectiveness of compression. This paper proposes a novel quantum information compression algorithm, termed "Dynamic Entropy Adjustment" (DEA), that dynamically adjusts the quantum state to maximize information utilization. We demonstrate that the algorithm's compression efficiency is directly proportional to the effectiveness of entropy adjustment, leading to improved data compression rates. The algorithm's core mechanism leverages the inherent sensitivity of quantum states to environmental fluctuations, enabling a more adaptive and efficient compression process. This paper explores the theoretical foundations of DEA and presents preliminary experimental results showcasing its superior performance compared to existing classical compression methods.

Jincheng Zhang

This paper introduces a novel quantum information compression protocol based on the inherent self-correlation of quantum states. Traditional information compression methods often rely on classical encoding, struggling to fully leverage the unique properties of quantum states. This protocol leverages these self-correlation characteristics, offering a pathway to significantly enhance compression efficiency and robustness in the face of environmental noise encountered during quantum information transmission. The core mechanism centers around designing a compression scheme that exploits the inherent properties of quantum states, resulting in improved compression rates and enhanced resilience against noise. The investigation explores the potential of this approach to address limitations of existing compression techniques, specifically focusing on adapting to the challenges posed by quantum systems.

pLalitha Nathp

Jincheng Zhang

This paper proposes a novel approach to quantum information compression based on fractal compression techniques. Quantum states, particularly those exhibiting inherent correlations and complex structures, possess a degree of self-similarity that can be effectively exploited. The core idea is to identify and represent these self-similar patterns using fractal compression algorithms, thereby reducing redundancy and improving storage efficiency. We demonstrate a theoretical framework for applying fractal compression to quantum information, outlining key compression algorithms and the mathematical principles underpinning the process. The efficiency of this method is evaluated through theoretical analysis and conceptual modeling, highlighting its potential advantages over traditional quantum compression methods. The core claim is that quantum information can be compressed with higher efficiency by exploiting the inherent fractal patterns within quantum states. The central mechanism involves developing compression algorithms that identify and exploit the self-similar structures within quantum states, reducing redundancy and improving storage efficiency. This work lays the groundwork for future research and development in fractal quantum compression.

Yun Kai Deng, Ming‐Xing Luo, S. Y.