Quantum networks are set to transform distributed communication and computation. However, achieving energy efficiency and resource optimization in large-scale networks is notably challenging because of the complexity of creating and routing quantum entanglement. This study presents a new quantum routing framework, Quantum Fidelity-Energy Optimized Routing (QFER), which dynamically balances energy use and fidelity loss to improve entanglement generation in diverse quantum networks. It employs a multiobjective optimization strategy that considers fidelity degradation, energy efficiency, and success probability, considering device variability, network traffic, and coherence limits. Real-time path optimization enables effective entanglement creation and optimal resource use in multi-hop networks. This approach significantly improves network scalability, throughput, and energy efficiency, supporting the development of resilient and sustainable quantum Internet frameworks that can meet the needs of advanced quantum communication systems.
Quantum networks are set to transform distributed communication and computation. However, achieving energy efficiency and resource optimization in large-scale networks is notably challenging because of the complexity of creating and routing quantum entanglement. This study presents a new quantum routing framework, Quantum Fidelity-Energy Optimized Routing (QFER), which dynamically balances energy use and fidelity loss to improve entanglement generation in diverse quantum networks. It employs a multiobjective optimization strategy that considers fidelity degradation, energy efficiency, and success probability, considering device variability, network traffic, and coherence limits. Real-time path optimization enables effective entanglement creation and optimal resource use in multi-hop networks. This approach significantly improves network scalability, throughput, and energy efficiency, supporting the development of resilient and sustainable quantum Internet frameworks that can meet the needs of advanced quantum communication systems.
Future quantum networks will enable the interconnection of multiple users distributed across vast geographic distances. Due to these large separations and limited physical resources, communication will often rely on multi-path routing strategies, where physical resources are distributed across channels of varying lengths and delivered to end users. Efficient long-distance quantum communication therefore requires optimizing the allocation of these resources across available paths. In this work, we introduce a performance-oriented approach to quantum network routing by extending the classical concept of Quality of Service (QoS) to the context of multi-path quantum resources distribution. Unlike prior models that consider entanglement generation or quantum memory coherence in isolation, we investigate the interplay between path assignment strategies, coherence time constraints, and quantum error correction (QEC), and how they jointly impact end-to-end communication fidelity. We analyze both unencoded and encoded transmissions over aggregated network paths, quantifying the effects of resource allocation on transmission success. Our findings show that fidelity cannot be optimized independently of memory lifetimes, and that while QEC can enhance performance under specific conditions, it also imposes additional constraints depending on network topology and path-length asymmetries. This work provides a foundation for developing QoS-aware quantum routing protocols that balance fidelity, throughput, and memory utilization -- key considerations for near-term quantum repeater networks.
Entanglement networks enable multi-user quantum communication by distributing entangled photon pairs. However, challenges such as uncertain entanglement establishment, resource contention, and fidelity loss hinder their performance. While existing studies address throughput and fidelity issues individually, an integrated approach remains absent. This paper presents the Entanglement Twice Routing with Fidelity Improvement (ETRFI) algorithm, a novel solution to simultaneously enhance throughput and fidelity. ETRFI dynamically allocates supplemental resources based on incremental throughput improvement and purification efficiency. In its second routing phase, entangled links are optimized to balance loss minimization and flow maximization. Furthermore, a time-slot control mechanism ensures synchronization and prevents decoherence during communication. Simulations confirm ETRFI's superior performance, with a 7% increase in entangled pair throughput and a 10% boost in valid throughput, outperforming prior methods.
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.
The quantum internet enables secure communication, distributed quantum computing, and remote sensing by distributing entanglement among distant nodes [1], [2]. However, establishing end-to-end entanglement across regional networks remains challenging because quantum links experience photon loss, entanglement generation is probabilistic, quantum memories have limited coherence times, and repeater resources are constrained [2], [3]. Conventional shortest-path routing does not jointly account for these quantum-specific limitations. This paper proposes a Fidelity- and Coherence-Aware Dynamic Entanglement Routing (FCADR) framework for regional quantum internet networks. The network is modelled as a graph containing end nodes, quantum repeaters, quantum memories, and lossy entanglement links. FCADR selects routes by jointly considering estimated end-to-end fidelity, entanglement-generation and swapping success probabilities, available quantum-memory capacity, and expected distribution delay. A discrete-event simulation framework is designed to compare FCADR with shortest-path routing, link-success-based routing, and fidelity-aware routing under varying channel loss, coherence time, traffic load, and node or link failures. Performance is evaluated using end-to-end fidelity, request-success rate, distribution delay, memory utilization, and network resilience. The framework aims to improve routing decisions by incorporating quantum-state quality and resource availability into dynamic path selection.
With the development of the Internet of Things (IoT) towards supporting next-generation security and distributed applications, the integration of quantum communication technology provides a promising way to enhance data security, computing power, and perception accuracy. The large-scale interconnection of quantum nodes enables the formation of quantum enhanced IoT networks, with high fidelity entanglement serving as the cornerstone of secure and efficient communication. However, the inherent limitations of quantum resources and channel noise pose significant challenges to routing and resource management. This article constructs the quantum path selection problem as a multi criteria path selection problem, designs a weight scoring function to obtain the optimal path in the current network for each request, and proposes a two-stage routing and allocation strategy TPROA, namely the idea of "guarantee before utilization", aiming to maximize network throughput and improve resource utilization in resource constrained network environments. The simulation results show that compared with traditional methods, TPROA significantly improves quantum resource utilization and network throughput. The proposed method lays the foundation for scalable quantum IoT integration.
The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.