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 Engineering

Efficient multi‐qubit quantum data compression

Chen‐Rui Fan, Bo Lü, Xue‐Ting Feng, Wei‐Chao Gao, Chuan Wang

Published May 5, 2021• 15 citations
Publisher

Abstract

Quantum data compression is of great significance to both quantum computation and quantum communication due to the limited resources for quantum storage. Here in this study, we present an efficient multi-qubit quantum data compression protocol in which N identically prepared qubits can be perfectly compressed into qubits. Moreover, the feasible implementation of the quantum data compression is also designed based on the IBM Quantum Experience. The experiment reveals efficient information compression performance using the current scheme. We believe this method might be useful in quantum information which one could make the encoding of information on the qubits more efficiently, and also reducing the resources consumption for quantum storage.

DOI
10.1002/que2.67

Related papers

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, 2026•0 citations

Quantum Data Compression of a Qubit Ensemble

Lee A. Rozema, Dylan H. Mahler, Alex Hayat +2 more

Data compression is a ubiquitous aspect of modern information technology, and the advent of quantum information raises the question of what types of compression are feasible for quantum data, where it is especially relevant given the extreme difficulty involved in creating reliable quantum memories. We present a protocol in which an ensemble of quantum bits (qubits) can in principle be perfectly compressed into exponentially fewer qubits. We then experimentally implement our algorithm, compressing three photonic qubits into two. This protocol sheds light on the subtle differences between quantum and classical information. Furthermore, since data compression stores all of the available information about the quantum state in fewer physical qubits, it could allow for a vast reduction in the amount of quantum memory required to store a quantum ensemble, making even today's limited quantum memories far more powerful than previously recognized.

arXiv:1410.3941•Physical Review Letters•Oct 17, 2014•58 citations

Quantum Entanglement-Based Data Compression & Transmission

Jincheng Zhang

This paper investigates the potential of quantum entanglement for data compression and transmission, aiming to surpass the limitations of classical methods. We propose a novel entanglement protocol that utilizes instantaneous correlations to represent data bits, incorporating quantum error correction to enhance reliability. The core mechanism focuses on encoding information via entangled particle states, suggesting a pathway for transmitting data across significant distances with enhanced efficiency and security. This work explores the theoretical foundations, algorithm design, and preliminary experimental results demonstrating the feasibility of this approach. The potential implications for data security and efficient information transfer are significant, representing a potential paradigm shift in data storage and communication technologies.

•Sep 6, 2026•0 citations

Information loss and run time from practical application of quantum data compression

Saahil Patel, Benjamin Collis, William Duong +4 more

Abstract We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBMs Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with density matrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for training and compressing our data on quantum devices, and relate trainability to specific characteristics and performance metrics of our parametric quantum circuits.

arXiv:2203.11332•Physica Scripta•Mar 15, 2023•7 citations

Efficient compression of quantum information

Martin Plesch, Vladimír Bužek

We propose a scheme for an exact efficient transformation of a tensor product state of many identically prepared qubits into a state of a logarithmically small number of qubits. Using a quadratic number of elementary quantum gates we transform $N$ identically prepared qubits into a state, which is nontrivial only on the first $\ensuremath{\lceil}\mathrm{log}{}_{2}(N+1)\ensuremath{\rceil}$ qubits. This procedure might be useful for quantum memories, as only a small portion of the original qubits has to be stored. Another possible application is in communicating a direction encoded in a set of quantum states, as the compressed state provides a high-effective method for such an encoding.

arXiv:0907.1764•Physical Review A•Mar 18, 2010•49 citations

Efficient Quantum Compression for Ensembles of Identically Prepared Mixed States

Yuxiang Yang, Giulio Chiribella, Daniel Ebler

We present one-shot compression protocols that optimally encode ensembles of N identically prepared mixed states into O(logN) qubits. In contrast to the case of pure-state ensembles, we find that the number of encoding qubits drops down discontinuously as soon as a nonzero error is tolerated and the spectrum of the states is known with sufficient precision. For qubit ensembles, this feature leads to a 25% saving of memory space. Our compression protocols can be implemented efficiently on a quantum computer.

arXiv:1506.03542•Physical Review Letters•Feb 23, 2016•28 citations
Quantum data compression is of great significance to both quantum computation and quantum communication due to the limited resources for quantum storage. Here in this study, we present an efficient multi-qubit quantum data compression protocol in which N identically prepared qubits can be perfectly compressed into qubits. Moreover, the feasible implementation of the quantum data compression is also designed based on the IBM Quantum Experience. The experiment reveals efficient information compression performance using the current scheme. We believe this method might be useful in quantum information which one could make the encoding of information on the qubits more efficiently, and also reducing the resources consumption for quantum storage.
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.
Data compression is a ubiquitous aspect of modern information technology, and the advent of quantum information raises the question of what types of compression are feasible for quantum data, where it is especially relevant given the extreme difficulty involved in creating reliable quantum memories. We present a protocol in which an ensemble of quantum bits (qubits) can in principle be perfectly compressed into exponentially fewer qubits. We then experimentally implement our algorithm, compressing three photonic qubits into two. This protocol sheds light on the subtle differences between quantum and classical information. Furthermore, since data compression stores all of the available information about the quantum state in fewer physical qubits, it could allow for a vast reduction in the amount of quantum memory required to store a quantum ensemble, making even today's limited quantum memories far more powerful than previously recognized.
This paper investigates the potential of quantum entanglement for data compression and transmission, aiming to surpass the limitations of classical methods. We propose a novel entanglement protocol that utilizes instantaneous correlations to represent data bits, incorporating quantum error correction to enhance reliability. The core mechanism focuses on encoding information via entangled particle states, suggesting a pathway for transmitting data across significant distances with enhanced efficiency and security. This work explores the theoretical foundations, algorithm design, and preliminary experimental results demonstrating the feasibility of this approach. The potential implications for data security and efficient information transfer are significant, representing a potential paradigm shift in data storage and communication technologies.
Abstract We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBMs Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with density matrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for training and compressing our data on quantum devices, and relate trainability to specific characteristics and performance metrics of our parametric quantum circuits.
We propose a scheme for an exact efficient transformation of a tensor product state of many identically prepared qubits into a state of a logarithmically small number of qubits. Using a quadratic number of elementary quantum gates we transform NNN identically prepared qubits into a state, which is nontrivial only on the first $\ensuremath{\lceil}\mathrm{log}{}_{2}(N+1)\ensuremath{\rceil}$ qubits. This procedure might be useful for quantum memories, as only a small portion of the original qubits has to be stored. Another possible application is in communicating a direction encoded in a set of quantum states, as the compressed state provides a high-effective method for such an encoding.
We present one-shot compression protocols that optimally encode ensembles of N identically prepared mixed states into O(logN) qubits. In contrast to the case of pure-state ensembles, we find that the number of encoding qubits drops down discontinuously as soon as a nonzero error is tolerated and the spectrum of the states is known with sufficient precision. For qubit ensembles, this feature leads to a 25% saving of memory space. Our compression protocols can be implemented efficiently on a quantum computer.