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

arXiv:2210.13751 · Physical Review A

Characterizing arbitrary quantum networks in the noisy intermediate-scale quantum era

Zhen‐Peng Xu

Published Oct 5, 2023• 7 citations
arXivHTMLPDF

Abstract

Quantum networks, as a playground for various quantum technologies, describe the distribution of quantum sources represented by edges to different parties represented by nodes in the network. Here, the author introduces a systematic approach to characterize arbitrary quantum networks in the noisy intermediate-scale quantum era, which can be noisy, intermediate-scale, random, and sparse.

DOI
10.1103/physreva.108.l040201

Related papers

Quantum Network Engineering in the NISQ Age: Principles, Missions, and Challenges

Uman Khalid, Junaid ur Rehman, Saw Nang Paing +3 more

The quantum internet is envisaged to connect quantum systems at a global scale to provide diverse applications unimaginable with the classical internet. However, the technological shortcomings and practical challenges of noisy intermediate-scale quantum (NISQ) devices impose several constraints on the vision of a truly global quantum internet. Recent experimental developments for quantum networks are laying the pathway from small-scale to intermediate-scale noisy quantum networks dedicated to specific operational tasks. Although these networks are not at the global scale, they are capable of providing a clear quantum advantage in their dedicated tasks. We explore the cardinal features of these NISQ networks, discuss their design constraints, and explore the possible applications they provide. We also outline some technical challenges that need to be addressed for the practical implementation of these networks and eventually lead toward a truly global, fully functional quantum internet.

IEEE Network•Oct 31, 2023•38 citations

Entanglement topography of large-scale quantum networks

Md Sohel Mondal, Dov Fields, Vladimir S. Malinovsky +1 more

Large-scale quantum networks, necessary for distributed quantum information processing, are posited to have quantum entangled systems between distant network nodes. The extent and quality of distributed entanglement in a quantum network, that is its functionality, depends on its topology, edge-parameter distributions and the distribution protocol. We uncover the parametric entanglement topography and introduce the notion of typical and maximal viable regions for entanglement-enabled tasks in a general model of large-scale quantum networks. We show that such a topographical analysis, in terms of viability regions, reveals important functional information about quantum networks, provides experimental targets for the edge parameters and can guide efficient quantum network design. Applied to a photonic quantum network, such a topographical analysis shows that in a network with radius $10^3$ kms and 1500 nodes, arbitrary pairs of nodes can establish quantum secure keys at a rate of $R_{sec}=1$ kHz using $1$ MHz entanglement generation sources on the edges and as few as 3 entanglement swappings at intermediate nodes along network paths.

arXiv:2312.16009•Dec 26, 2023•2 citations

Robustness of noisy quantum networks

Bruno Coelho Coutinho, William J. Munro, Kae Nemoto +1 more

Abstract Quantum networks allow us to harness networked quantum technologies and to develop a quantum internet. But how robust is a quantum network when its links and nodes start failing? We show that quantum complex networks based on typical noisy quantum-repeater nodes are prone to discontinuous phase transitions with respect to the random loss of operating links and nodes, abruptly compromising the connectivity of the network, and thus significantly limiting the reach of its operation. Furthermore, we determine the critical quantum-repeater efficiency necessary to avoid this catastrophic loss of connectivity as a function of the network topology, the network size, and the distribution of entanglement in the network. From all the network topologies tested, a scale-free network topology shows the best promise for a robust large-scale quantum internet.

arXiv:2103.03266•Communications Physics•Apr 28, 2022•34 citations

277Chapter 14 Quantum error mitigation for medical imaging noise reduction using GNNsgraph neural networks (GNNs)

E. Chandra Blessie, B Sundaravadivazhagan, V. Kumutha

Quantum computing is the latest emerging technology that is being used to solve complex problems more efficiently than classical computing technique classical computing technique s. It uses quantum principles of entanglement, superposition, and decoherence to achieve secure communication and computing. Quantum error mitigation quantum error mitigation comprises techniques to reduce the impact of errors in computations by enhancing the computational accuracy. In the current era of quantum computing known as the noisy intermediate-scale quantum (NISQ noisy intermediate-scale quantum (NISQ) ) era, it serves as an interim solution for researchers to develop and apply quantum algorithms for future advancement. Machine learning models are trained for correcting the errors in quantum computing. These models can learn the noise patterns and predict the necessary corrections, thereby enhancing the accuracy of quantum computations. Graph neural networks (GNNs graph neural networks (GNNs) ) offer a promising approach to this problem by exploiting their ability to model complex relationships and dependencies in data. Quantum-enhanced imaging quantum-enhanced imaging technologies promise significant advancements in medical diagnostics by offering improved resolution and sensitivity. However, these technologies are also prone to various forms of noise and errors, stemming from both quantum mechanical phenomena and conventional sources. This study explores the application of GNNs graph neural networks (GNNs) for quantum error mitigation to reduce noise in medical imaging, enhancing the accuracy and clarity of the results. GNNs graph neural networks (GNNs) are leveraged to model the complex structural relationships within medical imaging data, effectively learning and identifying noise patterns. By representing imaging data as a graph, where nodes correspond to pixels or voxels and edges represent spatial or relational connections, GNNs graph neural networks (GNNs) can predict and correct for errors. In this study, we review GNN based quantum error techniques for medical imaging. This work highlights the potential of GNNs graph neural networks (GNNs) as a powerful tool for enhancing quantum error resilience, providing a new direction for research and development in the quest for reliable and scalable quantum computing.

De Gruyter eBooks•Aug 9, 2025•0 citations

Characterizing large-scale quantum devices

Joel J. Wallman, Joseph Emerson

Characterizing large-scale quantum devices is challenging because quantum noise processes are complex. We present a method for efficiently characterizing general operations in quantum devices with signal-to-noise ratios independent of the system size.

Quantum Information and Measurement (QIM)•Jan 1, 2019•0 citations

Research status and future of quantum information technology

Guang‐Can Guo

Quantum information technology is an emerging interdisciplinary field of quantum mechanics and information science, whose birth will promote a new era of human intelligence from classical technology to quantum technology. This paper elaborates on the research status and future of the field of quantum information technology. Moreover, this paper shows the vision of quantum technology, i.e., the construction of various types of quantum networks, including quantum cloud computing networks, distributed quantum computing, quantum sensor networks, and quantum key distribution networks. Quantum computers have transformed research from the laboratory to the development of practical devices led by these enterprises. Currently, research and development of quantum computers have shifted to the stage of noisy intermediate-scale quantum computers (NISQ). In the era of quantum technology, there is no absolutely secure secrecy system or omnipotent way of deciphering Information and information security has entered a new stage of “quantum offense and defense."

Scientia Sinica Informationis•Sep 1, 2020•2 citations
Quantum networks, as a playground for various quantum technologies, describe the distribution of quantum sources represented by edges to different parties represented by nodes in the network. Here, the author introduces a systematic approach to characterize arbitrary quantum networks in the noisy intermediate-scale quantum era, which can be noisy, intermediate-scale, random, and sparse.
The quantum internet is envisaged to connect quantum systems at a global scale to provide diverse applications unimaginable with the classical internet. However, the technological shortcomings and practical challenges of noisy intermediate-scale quantum (NISQ) devices impose several constraints on the vision of a truly global quantum internet. Recent experimental developments for quantum networks are laying the pathway from small-scale to intermediate-scale noisy quantum networks dedicated to specific operational tasks. Although these networks are not at the global scale, they are capable of providing a clear quantum advantage in their dedicated tasks. We explore the cardinal features of these NISQ networks, discuss their design constraints, and explore the possible applications they provide. We also outline some technical challenges that need to be addressed for the practical implementation of these networks and eventually lead toward a truly global, fully functional quantum internet.
Large-scale quantum networks, necessary for distributed quantum information processing, are posited to have quantum entangled systems between distant network nodes. The extent and quality of distributed entanglement in a quantum network, that is its functionality, depends on its topology, edge-parameter distributions and the distribution protocol. We uncover the parametric entanglement topography and introduce the notion of typical and maximal viable regions for entanglement-enabled tasks in a general model of large-scale quantum networks. We show that such a topographical analysis, in terms of viability regions, reveals important functional information about quantum networks, provides experimental targets for the edge parameters and can guide efficient quantum network design. Applied to a photonic quantum network, such a topographical analysis shows that in a network with radius 10310^3103 kms and 1500 nodes, arbitrary pairs of nodes can establish quantum secure keys at a rate of Rsec=1R_{sec}=1Rsec​=1 kHz using 111 MHz entanglement generation sources on the edges and as few as 3 entanglement swappings at intermediate nodes along network paths.
Abstract Quantum networks allow us to harness networked quantum technologies and to develop a quantum internet. But how robust is a quantum network when its links and nodes start failing? We show that quantum complex networks based on typical noisy quantum-repeater nodes are prone to discontinuous phase transitions with respect to the random loss of operating links and nodes, abruptly compromising the connectivity of the network, and thus significantly limiting the reach of its operation. Furthermore, we determine the critical quantum-repeater efficiency necessary to avoid this catastrophic loss of connectivity as a function of the network topology, the network size, and the distribution of entanglement in the network. From all the network topologies tested, a scale-free network topology shows the best promise for a robust large-scale quantum internet.
Quantum computing is the latest emerging technology that is being used to solve complex problems more efficiently than classical computing technique classical computing technique s. It uses quantum principles of entanglement, superposition, and decoherence to achieve secure communication and computing. Quantum error mitigation quantum error mitigation comprises techniques to reduce the impact of errors in computations by enhancing the computational accuracy. In the current era of quantum computing known as the noisy intermediate-scale quantum (NISQ noisy intermediate-scale quantum (NISQ) ) era, it serves as an interim solution for researchers to develop and apply quantum algorithms for future advancement. Machine learning models are trained for correcting the errors in quantum computing. These models can learn the noise patterns and predict the necessary corrections, thereby enhancing the accuracy of quantum computations. Graph neural networks (GNNs graph neural networks (GNNs) ) offer a promising approach to this problem by exploiting their ability to model complex relationships and dependencies in data. Quantum-enhanced imaging quantum-enhanced imaging technologies promise significant advancements in medical diagnostics by offering improved resolution and sensitivity. However, these technologies are also prone to various forms of noise and errors, stemming from both quantum mechanical phenomena and conventional sources. This study explores the application of GNNs graph neural networks (GNNs) for quantum error mitigation to reduce noise in medical imaging, enhancing the accuracy and clarity of the results. GNNs graph neural networks (GNNs) are leveraged to model the complex structural relationships within medical imaging data, effectively learning and identifying noise patterns. By representing imaging data as a graph, where nodes correspond to pixels or voxels and edges represent spatial or relational connections, GNNs graph neural networks (GNNs) can predict and correct for errors. In this study, we review GNN based quantum error techniques for medical imaging. This work highlights the potential of GNNs graph neural networks (GNNs) as a powerful tool for enhancing quantum error resilience, providing a new direction for research and development in the quest for reliable and scalable quantum computing.
Quantum information technology is an emerging interdisciplinary field of quantum mechanics and information science, whose birth will promote a new era of human intelligence from classical technology to quantum technology. This paper elaborates on the research status and future of the field of quantum information technology. Moreover, this paper shows the vision of quantum technology, i.e., the construction of various types of quantum networks, including quantum cloud computing networks, distributed quantum computing, quantum sensor networks, and quantum key distribution networks. Quantum computers have transformed research from the laboratory to the development of practical devices led by these enterprises. Currently, research and development of quantum computers have shifted to the stage of noisy intermediate-scale quantum computers (NISQ). In the era of quantum technology, there is no absolutely secure secrecy system or omnipotent way of deciphering Information and information security has entered a new stage of “quantum offense and defense."