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.
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.
This paper explores the optimization of multi-path communication within quantum networks, aiming to enhance communication efficiency and reliability. The core concept involves distributing quantum information across multiple paths and employing an optimized algorithm to manage this distribution. The algorithm focuses on minimizing latency, maximizing throughput, and mitigating the effects of channel loss inherent in quantum communication. We present a novel approach utilizing a layered optimization strategy, combining shortest path routing with adaptive power allocation. Mathematical formulations are detailed, including metrics for path quality, latency, and overall network throughput. The proposed method offers a significant improvement over traditional single-path communication strategies, particularly in complex quantum network topologies. Simulation results (though not presented through experimental data) demonstrate a substantial increase in communication performance under various network conditions. Further research will explore integration with quantum error correction codes and dynamic network topology adaptation.
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 facilitate communication among quantum devices, playing a crucial role in enabling applications beyond the reach of classical networks. However, creating a quantum network that supports reliable long-distance entanglement distribution remains a significant challenge. Unlike classical networks, connections in quantum networks are established through entanglement generation. Effective communication between quantum devices requires a routing strategy that efficiently manages the establishment of entanglement. In addition to fidelity, the quality of communication is also determined by the capacity of the generated entanglement. Therefore, we proposed a capacity allocation scheduling scheme based on a metaheuristic approach, namely a Genetic Algorithm (GA), to ensure efficient capacity allocation for multiple communication paths within each communication request. Simulations were conducted in four different scenarios, and the results were compared to an existing capacity allocation scheduling algorithm, Progressive Filling (PF), to verify the effectiveness of the proposed scheme. The average capacity utilisation, U and weighted throughput, F results indicate that PF outperforms GA in a scenario with four communication requests, while GA performs better in scenarios involving six, eight, and ten communication requests. Specifically, the improvement in average capacity utilisation ranges from 8% to 46%, and the improvement in weighted throughput ranges from 24% to 94%. These results suggest that GA can serve as a valuable reference for designing optimal routing and capacity allocation schemes in more complex networks with multiple communication requests.
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.
Quantum network can provide end-to-end entanglements through quantum operations, supporting various quantum applications such as quantum key distribution and distributed quantum computing. However, many previous proposals for quantum networks assumed the division of quantum network into time slots and batch processing of network communication tasks, performing poorly in dynamic network scenarios and not fully utilizing network resources. This work focuses on the routing problem in entanglement quantum networks, aiming to provide more efficient end-to-end entanglement establishment for upper-layer applications. We propose an on-demand routing algorithm that dynamically adjusts the optimal path based on the current network conditions. Specifically, we consider the benefits of path width for quantum networks and propose an algorithm for finding the shortest wide path. Using this algorithm, each node establishes its own routing table. Additionally, to dynamically avoid network hotspots, we introduce the concept of traffic to reflect the busyness of nodes. Finally, using a dynamic cost calculation method as the routing metric, we select the optimal path for the current request. To evaluate the performance of the proposed quantum routing algorithm, we conducted simulation experiments on SimQN quantum simulation platform. The results show that our approach improves network throughput by 14 percent compared to existing solutions.
With the continuous expansion of quantum communication networks and the increasing diversity of their business types, how to achieve efficient configuration under limited quantum resources has become the key to improving system performance. The quantum resources described in this article mainly include entanglement pairs, quantum bits, and channel capacity. Traditional optimization methods typically rely on precise mathematical models, which are difficult to adapt to dynamically evolving network environments and susceptible to noise interference. Therefore, this study proposes a machine learning based framework for optimizing the allocation of quantum communication resources. This framework integrates deep reinforcement learning and graph neural network technology, enabling collaborative optimization of entanglement allocation, routing selection, and time slice scheduling between nodes. Three typical quantum communication scenarios were constructed and tested in a simulation environment, including static topology, dynamic links, and high noise environments. The proposed method was compared and analyzed with integer linear programming, heuristic greedy algorithms, and rule-based strategies. Experimental data shows that this research method achieved an improvement of approximately 12% to 18% in average fidelity retention, a 15% to 22% increase in resource utilization, and a 20% reduction in latency. This study provides useful reference for the intelligent resource management of large-scale quantum networks in the future.