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IEEE Transactions on Networking

Efficient and Flexible Multi-Qubit Entanglement Transmission in Quantum Networks

Yanan Gao, Song Yang, Fan Li, Youqi Li, Liehuang Zhu, Stojan Trajanovski, Xiaoming Fu

Published Nov 18, 2025• 1 citation
Publisher

Abstract

The unprecedented advancements in quantum technology have opened new prospects for the widespread adoption of quantum applications, placing new demands on the information transmission capabilities of large-scale quantum networks. Long-distance and stable entanglements are deemed as the lifeline in quantum network communication. However, some weaknesses, e.g., quantum decoherence, scarce quantum memory, and uneven-quality entanglement, of the quantum entanglement hinder the development. In this paper, we proposeSophon, an online transmission framework for quantum networks, which utilizes high-dimensional entanglements to concurrently transmit multi-qubit data to satisfy the transmission requirements of the real-time request set. We first model the quantum network with multi-qubit entanglement represented by$W$quantum state and then formulate the Entanglement Routing and Qubit Provisioning (ERQP) problem as a global-local optimization process. To solve theERQPproblem, we distributedly regard each network node as an RL agent for resource provisioning and extend the step-updating of the Markov Decision Process by introducing a centralized controller for entanglement route selection to optimize local and global objectives, respectively. Extensive simulations demonstrate, on the self-made simulation platform,Sophonachieves a$21.89\%-66.52\%$decrease in the communication cost, and is more robust on different scales of the network topology and the request set than the baselines.

DOI
10.1109/ton.2025.3631515

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The unprecedented advancements in quantum technology have opened new prospects for the widespread adoption of quantum applications, placing new demands on the information transmission capabilities of large-scale quantum networks. Long-distance and stable entanglements are deemed as the lifeline in quantum network communication. However, some weaknesses, e.g., quantum decoherence, scarce quantum memory, and uneven-quality entanglement, of the quantum entanglement hinder the development. In this paper, we proposeSophon, an online transmission framework for quantum networks, which utilizes high-dimensional entanglements to concurrently transmit multi-qubit data to satisfy the transmission requirements of the real-time request set. We first model the quantum network with multi-qubit entanglement represented byWWWquantum state and then formulate the Entanglement Routing and Qubit Provisioning (ERQP) problem as a global-local optimization process. To solve theERQPproblem, we distributedly regard each network node as an RL agent for resource provisioning and extend the step-updating of the Markov Decision Process by introducing a centralized controller for entanglement route selection to optimize local and global objectives, respectively. Extensive simulations demonstrate, on the self-made simulation platform,Sophonachieves a21.89%−66.52%21.89\%-66.52\%21.89%−66.52%decrease in the communication cost, and is more robust on different scales of the network topology and the request set than the baselines.
The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.
Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.
Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.
Quantum nodes exchange qubits to achieve largescale quantum computing applications in a quantum network (QN). However, due to environmental influences, entangled links used for data transmission would decohere over time. Besides, the scarcity of quantum memory in each quantum node necessitates frequent communication between nodes, leading to significant variations in the traffic matrix. Thus, providing real-time services in QNs becomes particularly important. To this end, this paper presents a new optimization problem and proposes a novel online algorithm, which combines the game-theoretic analysis results with the linear programming (LP) by the primal-dual technique. Finally, the simulation results demonstrate that the proposed algorithm outperforms the other baselines by up to 90 %.
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.
Quantum Data Networks (QDNs) typically leverage teleportation to reliably send data quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one entanglement connection between Alice and Bob needs to be established. Accordingly, we have to establish as many entanglement connections as possible with limited quantum resources in order to maximize the network throughput. Conventional methods assume a known traffic matrix and calculate the paths to establish entanglement connections in one batch using a centralized algorithm. However, these methods are not scalable in large scale QDNs since it is time consuming to optimize the entanglement paths for a batch of requests, which may result in a long time slot duration and significantly reduce the network throughput. To address this issue, we propose an Online Entanglement Routing (OER) scheme which determines the entanglement paths for each request when it arrives. In addition, OER pursues work conservation and fairness among all requests in the QDNs. Through extensive simulations, we demonstrate that OER not only outperforms two representative heuristics by up to 61.27% and 52.79%, respectively, in terms of average request completion time, but also achieves a better fairness performance than these two counterparts.