Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum-Enhanced Data Compression with Entangled State Encoding

Jincheng Zhang

Published Aug 28, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.5281/zenodo.22136351

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, 2026•0 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, 2026•0 citations

Quantum-Inspired Data Compression via Entangled State Encoding

Jincheng Zhang

This paper proposes a novel data compression technique inspired by quantum entanglement. Traditional data compression algorithms often struggle to effectively capture and utilize complex correlations present within data sets. Our approach leverages the concept of entangled states to represent data, allowing multiple data points to be encoded simultaneously within a single quantum-like state. This fundamentally shifts the paradigm of compression from relying solely on statistical redundancy to exploiting inherent correlations, potentially leading to significantly higher compression ratios compared to conventional methods. The core idea is to map data elements to the amplitudes of an entangled state, effectively encoding multiple values with a single state vector. We explore the mathematical framework underlying this encoding process, including the construction of entangled states and the optimization strategies for data representation. The proposed method's potential impact on various data compression applications is discussed.

•Sep 7, 2026•0 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, 2026•0 citations

Quantum Entanglement-Driven Distributed Compression

Jincheng Zhang

This paper proposes a novel approach to distributed compression leveraging the unique properties of quantum entanglement. Traditional distributed compression techniques rely heavily on classical communication and shared local information, often limited by the Shannon limit. We introduce a framework where data is encoded into entangled states between multiple quantum systems. By performing measurements and implementing correction protocols on these entangled states, we can extract shared information while maintaining the entanglement correlation. This process effectively reduces data redundancy, leading to compression. The key innovation lies in utilizing the inherent non-locality of entanglement for direct data compression without the constraints of classical communication channels. We outline the theoretical foundations, explore the potential compression rates, and discuss the challenges and future directions of this entanglement-driven approach. The core claim is that this method can achieve compression rates exceeding those possible with classical methods, offering a fundamentally new paradigm for data compression. ---

•Sep 1, 2026•0 citations

Quantum Entanglement-Based Data Compression via Phase Space Mapping

Jincheng Zhang

This paper proposes a novel data compression technique leveraging the unique properties of quantum entanglement. The core idea involves encoding data into the correlated phase space of entangled qubits, achieving lossless compression. The algorithm utilizes controlled phase manipulation for both encoding and decoding, capitalizing on the inherent correlations between entangled particles. Unlike traditional entropy-based compression methods, this approach directly exploits quantum mechanical phenomena. We demonstrate a theoretical framework and outline a compression algorithm with the potential for significant improvements in compression ratios. The key mathematical representation of the compression process is defined as follows: Let $x = (x_1, x_2, ..., x_N)$ be the original data sequence, where each $x_i$ is a binary value (0 or 1). The compression algorithm maps this sequence to an entangled state $(|\psi_1\rangle, |\psi_2\rangle, ..., |\psi_N\rangle)$, where $|\psi_i\rangle$ represents the state of the *i*-th qubit in the entangled pair. The compression ratio *R* is defined as: $R = \frac{N}{N_{encoded}}$ where *N* is the length of the original data sequence and *Nencoded* is the number of qubits used to represent the data. The encoding process is described by: $|\psi_i\rangle = \alpha_i |0\rangle + \beta_i |1\rangle$ where $\alpha_i$ and $\beta_i$ are complex amplitudes determined by the data bit $x_i$. The decoding process reverses this operation, reconstructing the original data from the measured state of the qubits. The fidelity of the reconstruction is crucial, and the algorithm incorporates techniques to minimize errors due to measurement noise. The overall compression algorithm can be summarized as follows:

•Sep 6, 2026•0 citations
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.
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. ---
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.
This paper proposes a novel data compression technique inspired by quantum entanglement. Traditional data compression algorithms often struggle to effectively capture and utilize complex correlations present within data sets. Our approach leverages the concept of entangled states to represent data, allowing multiple data points to be encoded simultaneously within a single quantum-like state. This fundamentally shifts the paradigm of compression from relying solely on statistical redundancy to exploiting inherent correlations, potentially leading to significantly higher compression ratios compared to conventional methods. The core idea is to map data elements to the amplitudes of an entangled state, effectively encoding multiple values with a single state vector. We explore the mathematical framework underlying this encoding process, including the construction of entangled states and the optimization strategies for data representation. The proposed method's potential impact on various data compression applications is discussed.
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.
This paper proposes a novel approach to distributed compression leveraging the unique properties of quantum entanglement. Traditional distributed compression techniques rely heavily on classical communication and shared local information, often limited by the Shannon limit. We introduce a framework where data is encoded into entangled states between multiple quantum systems. By performing measurements and implementing correction protocols on these entangled states, we can extract shared information while maintaining the entanglement correlation. This process effectively reduces data redundancy, leading to compression. The key innovation lies in utilizing the inherent non-locality of entanglement for direct data compression without the constraints of classical communication channels. We outline the theoretical foundations, explore the potential compression rates, and discuss the challenges and future directions of this entanglement-driven approach. The core claim is that this method can achieve compression rates exceeding those possible with classical methods, offering a fundamentally new paradigm for data compression. ---
This paper proposes a novel data compression technique leveraging the unique properties of quantum entanglement. The core idea involves encoding data into the correlated phase space of entangled qubits, achieving lossless compression. The algorithm utilizes controlled phase manipulation for both encoding and decoding, capitalizing on the inherent correlations between entangled particles. Unlike traditional entropy-based compression methods, this approach directly exploits quantum mechanical phenomena. We demonstrate a theoretical framework and outline a compression algorithm with the potential for significant improvements in compression ratios. The key mathematical representation of the compression process is defined as follows: Let x=(x1,x2,...,xN)x = (x_1, x_2, ..., x_N)x=(x1​,x2​,...,xN​) be the original data sequence, where each xix_ixi​ is a binary value (0 or 1). The compression algorithm maps this sequence to an entangled state (∣ψ1⟩,∣ψ2⟩,...,∣ψN⟩)(|\psi_1\rangle, |\psi_2\rangle, ..., |\psi_N\rangle)(∣ψ1​⟩,∣ψ2​⟩,...,∣ψN​⟩), where ∣ψi⟩|\psi_i\rangle∣ψi​⟩ represents the state of the *i*-th qubit in the entangled pair. The compression ratio *R* is defined as: R=NNencodedR = \frac{N}{N_{encoded}}R=Nencoded​N​ where *N* is the length of the original data sequence and *Nencoded* is the number of qubits used to represent the data. The encoding process is described by: ∣ψi⟩=αi∣0⟩+βi∣1⟩|\psi_i\rangle = \alpha_i |0\rangle + \beta_i |1\rangle∣ψi​⟩=αi​∣0⟩+βi​∣1⟩ where αi\alpha_iαi​ and βi\beta_iβi​ are complex amplitudes determined by the data bit xix_ixi​. The decoding process reverses this operation, reconstructing the original data from the measured state of the qubits. The fidelity of the reconstruction is crucial, and the algorithm incorporates techniques to minimize errors due to measurement noise. The overall compression algorithm can be summarized as follows: