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IEEE Transactions on Networking

NarrowGap: Reducing Bottlenecks for End-to-End Entanglement Distribution in Quantum Networks

Zhonghui Li, Jian Li, Kaiping Xue, Lutong Chen, Nenghai Yu, Qibin Sun, Jun Lu

Published Oct 16, 2024• 9 citations
Publisher

Abstract

Quantum networks, which work by establishing entanglement between distant quantum end nodes (known as end-to-end entanglement distribution), are the promising infrastructure for quantum applications. However, the inherent loss in quantum channels and quantum decoherence contribute to the scarcity of entanglement resources in quantum networks. Consequently, there is an inevitable gap between available entanglement resources and requests’ demands, significantly hindering concurrent end-to-end entanglement distributions. In this paper, we present NarrowGap, an end-to-end entanglement distribution design that can alleviate the negative impact of entanglement resource scarcity on the request service capability of quantum networks. At the heart of NarrowGap, the resource transfer scheme (RTS) is designed to transfer idle entanglement resources to boost the bottlenecks’ capacities based on the unique feature of entanglement swapping, thus narrowing the gap between available entanglement resources and requests’ demands for end-to-end entanglements. Besides, NarrowGap presents a resource allocation scheme (RAS) to guarantee fairness, considering both the success probability of end-to-end entanglement distribution and each request’s demand, to address resource competition in bottlenecks. Extensive simulations demonstrate that NarrowGap outperforms three representative schemes and can achieve more than twice the performance improvement in request service rate.

DOI
10.1109/tnet.2024.3476342

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Quantum networks, which work by establishing entanglement between distant quantum end nodes (known as end-to-end entanglement distribution), are the promising infrastructure for quantum applications. However, the inherent loss in quantum channels and quantum decoherence contribute to the scarcity of entanglement resources in quantum networks. Consequently, there is an inevitable gap between available entanglement resources and requests’ demands, significantly hindering concurrent end-to-end entanglement distributions. In this paper, we present NarrowGap, an end-to-end entanglement distribution design that can alleviate the negative impact of entanglement resource scarcity on the request service capability of quantum networks. At the heart of NarrowGap, the resource transfer scheme (RTS) is designed to transfer idle entanglement resources to boost the bottlenecks’ capacities based on the unique feature of entanglement swapping, thus narrowing the gap between available entanglement resources and requests’ demands for end-to-end entanglements. Besides, NarrowGap presents a resource allocation scheme (RAS) to guarantee fairness, considering both the success probability of end-to-end entanglement distribution and each request’s demand, to address resource competition in bottlenecks. Extensive simulations demonstrate that NarrowGap outperforms three representative schemes and can achieve more than twice the performance improvement in request service rate.
Entanglement distribution between distant quantum nodes plays an important role in quantum networks. However, due to the unique properties of quantum mechanics and hardware limitations, entanglement resources in quantum networks are scarce. Quantum links that fail to meet request demands become bottleneck links, significantly hindering remote entanglement distribution in multi-request scenarios. In this paper, we propose an entanglement routing scheme called SwappingBoost that can effectively reduce resource consumption along entanglement distribution paths, alleviating the negative impact of bottleneck links. SwappingBoost first employs a decreasing resource reservation method to compensate for resource losses caused by failed entanglement swapping, freeing up pre-reserved resources on downstream links to accommodate other paths and requests. Besides, SwappingBoost introduces a path-priority-based rounding algorithm that achieves integer-level resource allocation while ensuring balanced resource allocation. Extensive simulation results demonstrate that SwappingBoost can effectively reduce the load of bottleneck links, enhancing network throughput while maintaining fairness among multiple requests.
The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.
Quantum entanglement networks has ushered in a new era of communication, with the core task of generating long-distance quantum entanglement being crucial for a multitude of quantum applications. Although current research is primarily focused on establishing long-distance entanglement across various network topologies, there is still a lack of attention to the variability of communication requests within quantum networks. This paper introduces the Fair and Restricted Resource Allocation Algorithm (FARA), designed to meet the business-driven resource allocation requirements within quantum networks. This algorithm prioritizes the demands and urgency of requests to establish a fairer response mechanism, leading to an enhanced user experience. We refine the strategy for allocating network resources to maximize their use during multiple concurrent requests and to improve the success rate of business. Simulation results demonstrate that our proposed scheme improves the business success rate by 6.35% to 24.58% and user satisfaction by 0.922 to 4.533 points compared to the Q-CAST algorithm. Additionally, resource wastage is reduced by 473 to 800 units. These enhancements indicate that our research provides effective strategies for business-driven optimization in quantum networks and makes a positive contribution to the development of entanglement networks towards more practical and efficient applications.
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.
Remote entanglement distribution plays a crucial role in quantum networks, which can support many essential and exciting quantum applications. As the network scale expands, it is urgent to design a general and efficient remote entanglement distribution protocol. Nowadays, connection-oriented remote entanglement distribution protocols are available to achieve reliable entanglement distribution. However, in memory-limited quantum networks, connection-oriented entanglement distribution protocols cannot utilize entanglement resources fully and increase the delay of End-to-End (E2E) entanglement connection establishment. To overcome these shortcomings of existing entanglement distribution protocols, we design a connectionless remote entanglement distribution protocol to let Source-Destination (S-D) pairs compete for entanglement resources simultaneously. In our protocol, a fair request scheduling algorithm is proposed to reduce the waiting time without entanglement connections between S-D pairs. Furthermore, a fast scheduling trigger mechanism is proposed to perform entanglement swapping timely to reduce the delay of E2E entanglement connection establishment. The simulation results show that the designed protocol has advantages in terms of resource utilization, throughput, the service completion time of S-D pairs, and the maximum waiting time, compared with the existing remote entanglement distribution protocol.
Quantum networks are designed to transmit quantum bits (qubits) among quantum devices to enable new network resources for the applications. Entanglement distribution and entanglement swapping are fundamental procedures that are required in several network operations. However, they are probabilistic operations, which can lead to severe network performance degradation. This article investigates the engineering problem of resource allocation in quantum networks, considering factors like entanglement distribution probability, quantum memory characteristics, and fidelity. We model this as an optimization model to obtain an optimal solution. In particular, we formulate an integer linear programming (ILP) and develop a heuristic algorithm, aiming to minimize the number of required entangled qubit pairs (Bell pairs or EPR pairs) in any adjacent pair in the quantum network. Extensive simulations are performed to compare the performance of proposed ILP and heuristic. In all the cases, the heuristic produces a comparable solution to the optimal one. Simulation results ensure that the value of maximum utilized Bell pairs in a quantum network highly depends on the value of the probability of entangled pairs established, considering the time in the quantum memory besides the number of incoming requests.