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:2510.15776

Flexible Qubit Allocation of Network Resource States

Mazza, Francesco, Jorge Miguel-Ramiro, Jessica Illiano, Alexander Pirker, Caleffi, Marcello, Angela Sara Cacciapuoti, Wolfgang Dür

Published Oct 17, 2025• 0 citations
arXivHTMLPDF

Abstract

The Quantum Internet is still in its infancy, yet identifying scalable and resilient quantum network resource states is an essential task for realizing it. We explore the use of graph states with flexible, non-trivial qubit-to-node assignments. This flexibility enables adaptable engineering of the entanglement topology of an arbitrary quantum network. In particular, we focus on cluster states with arbitrary allocation as network resource states and as a promising candidate for a \textit{network core}-level entangled resource, due to its intrinsic flexible connectivity properties and resilience to particle losses. We introduce a modeling framework for overlaying entanglement topologies on physical networks and demonstrate how optimized and even random qubit assignment creates shortcuts and improves robustness and memory savings, while reducing the worst-case hop distance between remote network nodes, when compared to conventional approaches.

DOI
10.48550/arxiv.2510.15776

Related papers

A Quantum Overlay Network for Efficient Entanglement Distribution

Shahrooz Pouryousef, Nitish K. Panigrahy, Don Towsley

Distributing quantum entanglements over long distances is essential for the realization of a global scale quantum Internet. Most of the prior work and proposals assume an on-demand distribution of entanglements which may result in significant network resource under-utilization. In this work, we introduce Quantum Overlay Networks (QONs) for efficient entanglement distribution in quantum networks. When the demand to create end-to-end user entanglements is low, QONs can generate and store maximally entangled Bell pairs (EPR pairs) at specific overlay storage nodes of the network. Later, during peak demands, requests can be served by performing entanglement swaps either over a direct path from the network or over a path using the storage nodes. We solve the link entanglement and storage resource allocation problem in such a QON using a centralized optimization framework. We evaluate the performance of our proposed QON architecture over a wide number of network topologies under various settings using extensive simulation experiments. Our results demonstrate that QONs fare well by a factor of 40% with respect to meeting surge and changing demands compared to traditional non-overlay proposals. QONs also show significant improvement in terms of average entanglement request service delay over non-overlay approaches.

arXiv:2212.01694•IEEE Conference on Computer Communications (INFOCOM)•May 17, 2023•36 citations

Dynamic topology resilience for quantum networks

László Gyöngyösi, Sándor Imre

In quantum repeater networks, the varying stability of entangled quantum links makes dynamic topology resilience an emerging issue. Here we define an efficient topology adaption method for quantum repeater networks. The model assumes the random failures of entangled links and several parallel demands from legal users. The shortest path defines a set of entangled links for which the probability of stability is above a critical threshold. The scheme is utilized in a base-graph of the overlay quantum network to provide an efficient shortest path selection for the demands of all users of the network. We study the problem of entanglement assignment in a quantum repeater network, prove its computational complexity, and show an optimization procedure. The results are particularly convenient for future quantum networking, quantum-internet, and experimental long-distance quantum communications.

Quantum Computing, Communication, and Simulation•Feb 22, 2018•18 citations

Entangled Resource Allocation Algorithm for Quantum Networks

Jincheng Zhang

This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.

•Aug 31, 2026•0 citations

Concurrent Multiple Entanglement Routing Model with Function-Configurable Nodes in Quantum Networks

Ryuta Shiraki, Bijoy Chand Chatterjee, Eiji Oki

In traditional quantum networks, transmitter/receiver (TR) and repeater (REP) roles in quantum networks are assigned to separate physical nodes due to distinct hardware requirements However, advances in integrated quantum processors now enable nodes to switch between these roles. This paper proposes a quantum network model for concurrent entanglement routing that exploits this capability, allowing each node to function flexibly as both a TR and a REP within a multilayered graph framework. The model is formulated as an optimization problem aimed at maximizing the number of successful entanglement connections for a given set of requests. Logical TR and REP roles within the same physical node share quantum memory and fiber resources, leading to more efficient resource utilization. Numerical experiments show that the proposed model offers improved routing flexibility and better resource efficiency compared to existing benchmark models.

IEEE International Conference on Advanced Networks and Telecommunications Systems•Dec 15, 2025•0 citations

Quantum LAN: On-Demand Network Topology via Two-colorable Graph States

Francesco Mazza, Marcello Caleffi, Angela Sara Cacciapuoti

In Quantum Local Area Networks (QLANs) - the near-term building block of the Quantum Internet - the choice of the physical network topology is not so restrictive and binding as in classical LAN in terms of communication capabilities among the network nodes. And the rationale for this is the entanglement-enabled connectivity induced by multipartite entangled states. Indeed, it is possible to create on-demand links between the QLAN nodes by properly manipulating the shared multipartite entangled states. Thus, it is possible to build an overlay entangled network upon the physical one, characterized by a topology different from the physical one and referred to as artificial topology. In this paper we address the fundamental issue of engineering the artificial topology to bypass the limitations induced by the physical network topology. This possibility to overcome the constraints in the network design induced by the physical topology has no counterpart in classical networks. To this aim, we exploit the properties of a class of multipartite entangled states, referred to as graph states, which exhibit unique properties, making them very interesting and promising for engineering artificial topologies in QLANs.

International Conference on Quantum Communications, Networking, and Computing (QCNC)•Jul 1, 2024•9 citations

Quantum Entanglement-Based Resource Allocation

Jincheng Zhang

This paper proposes a novel approach to resource allocation in distributed computing systems utilizing the unique properties of quantum entanglement. Traditional resource allocation methods are often hampered by latency issues stemming from signal propagation delays within networks. This research introduces a system where quantum entanglement is directly harnessed to establish instantaneous correlations between computing nodes. These correlations enable real-time assessment of resource availability and demand, facilitating dynamic and optimized resource assignments. The core mechanism involves creating entangled pairs, followed by employing entanglement witness measurements to determine optimal allocations. The system fundamentally shifts from relying on communication-based routing to leveraging quantum correlations for optimization. This approach promises significantly reduced latency and improved overall system efficiency compared to conventional methods. Mathematical models are presented to illustrate the core concepts and theoretical performance bounds. The key performance indicators (KPIs) investigated include allocation latency, resource utilization, and overall system throughput. The results demonstrate the potential for entanglement-based resource allocation to outperform traditional approaches under various network conditions.

•Aug 29, 2026•0 citations
The Quantum Internet is still in its infancy, yet identifying scalable and resilient quantum network resource states is an essential task for realizing it. We explore the use of graph states with flexible, non-trivial qubit-to-node assignments. This flexibility enables adaptable engineering of the entanglement topology of an arbitrary quantum network. In particular, we focus on cluster states with arbitrary allocation as network resource states and as a promising candidate for a \textit{network core}-level entangled resource, due to its intrinsic flexible connectivity properties and resilience to particle losses. We introduce a modeling framework for overlaying entanglement topologies on physical networks and demonstrate how optimized and even random qubit assignment creates shortcuts and improves robustness and memory savings, while reducing the worst-case hop distance between remote network nodes, when compared to conventional approaches.
Distributing quantum entanglements over long distances is essential for the realization of a global scale quantum Internet. Most of the prior work and proposals assume an on-demand distribution of entanglements which may result in significant network resource under-utilization. In this work, we introduce Quantum Overlay Networks (QONs) for efficient entanglement distribution in quantum networks. When the demand to create end-to-end user entanglements is low, QONs can generate and store maximally entangled Bell pairs (EPR pairs) at specific overlay storage nodes of the network. Later, during peak demands, requests can be served by performing entanglement swaps either over a direct path from the network or over a path using the storage nodes. We solve the link entanglement and storage resource allocation problem in such a QON using a centralized optimization framework. We evaluate the performance of our proposed QON architecture over a wide number of network topologies under various settings using extensive simulation experiments. Our results demonstrate that QONs fare well by a factor of 40% with respect to meeting surge and changing demands compared to traditional non-overlay proposals. QONs also show significant improvement in terms of average entanglement request service delay over non-overlay approaches.
In quantum repeater networks, the varying stability of entangled quantum links makes dynamic topology resilience an emerging issue. Here we define an efficient topology adaption method for quantum repeater networks. The model assumes the random failures of entangled links and several parallel demands from legal users. The shortest path defines a set of entangled links for which the probability of stability is above a critical threshold. The scheme is utilized in a base-graph of the overlay quantum network to provide an efficient shortest path selection for the demands of all users of the network. We study the problem of entanglement assignment in a quantum repeater network, prove its computational complexity, and show an optimization procedure. The results are particularly convenient for future quantum networking, quantum-internet, and experimental long-distance quantum communications.
This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
In traditional quantum networks, transmitter/receiver (TR) and repeater (REP) roles in quantum networks are assigned to separate physical nodes due to distinct hardware requirements However, advances in integrated quantum processors now enable nodes to switch between these roles. This paper proposes a quantum network model for concurrent entanglement routing that exploits this capability, allowing each node to function flexibly as both a TR and a REP within a multilayered graph framework. The model is formulated as an optimization problem aimed at maximizing the number of successful entanglement connections for a given set of requests. Logical TR and REP roles within the same physical node share quantum memory and fiber resources, leading to more efficient resource utilization. Numerical experiments show that the proposed model offers improved routing flexibility and better resource efficiency compared to existing benchmark models.
In Quantum Local Area Networks (QLANs) - the near-term building block of the Quantum Internet - the choice of the physical network topology is not so restrictive and binding as in classical LAN in terms of communication capabilities among the network nodes. And the rationale for this is the entanglement-enabled connectivity induced by multipartite entangled states. Indeed, it is possible to create on-demand links between the QLAN nodes by properly manipulating the shared multipartite entangled states. Thus, it is possible to build an overlay entangled network upon the physical one, characterized by a topology different from the physical one and referred to as artificial topology. In this paper we address the fundamental issue of engineering the artificial topology to bypass the limitations induced by the physical network topology. This possibility to overcome the constraints in the network design induced by the physical topology has no counterpart in classical networks. To this aim, we exploit the properties of a class of multipartite entangled states, referred to as graph states, which exhibit unique properties, making them very interesting and promising for engineering artificial topologies in QLANs.
This paper proposes a novel approach to resource allocation in distributed computing systems utilizing the unique properties of quantum entanglement. Traditional resource allocation methods are often hampered by latency issues stemming from signal propagation delays within networks. This research introduces a system where quantum entanglement is directly harnessed to establish instantaneous correlations between computing nodes. These correlations enable real-time assessment of resource availability and demand, facilitating dynamic and optimized resource assignments. The core mechanism involves creating entangled pairs, followed by employing entanglement witness measurements to determine optimal allocations. The system fundamentally shifts from relying on communication-based routing to leveraging quantum correlations for optimization. This approach promises significantly reduced latency and improved overall system efficiency compared to conventional methods. Mathematical models are presented to illustrate the core concepts and theoretical performance bounds. The key performance indicators (KPIs) investigated include allocation latency, resource utilization, and overall system throughput. The results demonstrate the potential for entanglement-based resource allocation to outperform traditional approaches under various network conditions.