The quantum internet facilitates communication between quantum devices, making its development essential for advancing quantum technologies. However, realizing a fully functional quantum internet requires overcoming several challenges, including establishing efficient routing mechanisms to ensure effective communication. This study proposes a capacity allocation scheduling scheme based on a Genetic Algorithm (GA) to address the issue of non-uniform edge capacity utilization within a practical routing process. However, it is essential to note that the performance of the GA scheduling scheme is influenced by its control parameters, highlighting the need for parameter tuning. In this paper, we conducted simulations to examine how epochs and population sizes impact the GA scheduling performance, specifically in maximizing average capacity utilization and weighted throughput . The configurations were simulated under six and ten communication requests across two topology structures to assess their response to variations in problem characteristics. The simulation results were then compared with an existing capacity allocation scheduling method, Progressive Filling (PF). Our findings reveal that GA outperforms PF in all scenarios and topology structures. Additionally, we deduce that 30 epochs and a population size of 50 are adequate for optimizing average capacity utilization and weighted throughput .
The quantum internet facilitates communication between quantum devices, making its development essential for advancing quantum technologies. However, realizing a fully functional quantum internet requires overcoming several challenges, including establishing efficient routing mechanisms to ensure effective communication. This study proposes a capacity allocation scheduling scheme based on a Genetic Algorithm (GA) to address the issue of non-uniform edge capacity utilization within a practical routing process. However, it is essential to note that the performance of the GA scheduling scheme is influenced by its control parameters, highlighting the need for parameter tuning. In this paper, we conducted simulations to examine how epochs and population sizes impact the GA scheduling performance, specifically in maximizing average capacity utilization and weighted throughput . The configurations were simulated under six and ten communication requests across two topology structures to assess their response to variations in problem characteristics. The simulation results were then compared with an existing capacity allocation scheduling method, Progressive Filling (PF). Our findings reveal that GA outperforms PF in all scenarios and topology structures. Additionally, we deduce that 30 epochs and a population size of 50 are adequate for optimizing average capacity utilization and weighted throughput .
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.
Abstract Quantum network is a promising platform for many ground-breaking applications that lie beyond the capability of its classical counterparts. Efficient entanglement generation on quantum networks with relatively limited resources such as quantum memories is essential to fully realize the network’s capabilities, the solution to which calls for delicate network design and is currently at the primitive stage. In this study we propose an effective routing scheme to enable automatic responses for multiple requests of entanglement generation between source-terminal stations on a quantum lattice network with finite edge capacities. Multiple connection paths are exploited for each connection request while entanglement fidelity is ensured for each path by performing entanglement purification. The routing scheme is highly modularized with a flexible nature, embedding quantum operations within the algorithmic workflow, whose performance is evaluated from multiple perspectives. In particular, three algorithms are proposed and compared for the scheduling of capacity allocation on the edges of quantum network. Embodying the ideas of proportional share and progressive filling that have been well-studied in classical routing problems, we design another scheduling algorithm, the propagatory update method, which in certain aspects overrides the two algorithms based on classical heuristics in scheduling performances. The general solution scheme paves the road for effective design of efficient routing and flow control protocols on applicational quantum networks.
There is an ever-growing need for optimizing efficient nano and quantum communication systems and this demand calls for more advanced optimization techniques than those that have been around for quite some time. The conventional genetic algorithms (GAs) proved to be quite helpful in general but proved to be poor when it comes to fast convergence and adaptability to the changing communication environment. Moreover, fault tolerance and resource efficiency in these communication channels are poorly addressed, giving rise to a huge roadblock to practical implementations in a number of different use cases. This research presents a set of novel hybrid optimization methods aimed at addressing the issues previously mentioned. In the first place, a Quantum-inspired Genetic Algorithm (QIGA) with Simulated Annealing (SA) Initialization is proposed through augmenting traditional genetic operations with the quantum computing paradigm. In the first instance, the hybrid approach works by utilizing the parameters derived from SA while initializing the GA population, hence effective exploration of solution space. Additionally, the Adaptive Genetic Algorithm with Reinforcement Learning (AGA-RL) is presented, which uses real-time feedback from the current environment to modify the GA parameters. This way, the system will be much more adaptive and hence be able to adjust the GA so that it can match the rapidly changing channel conditions. Simultaneously, the Fault-Tolerant Genetic Algorithm with Error-Correcting Codes (FTGA-ECC) is developed to ensure reliable data transmission, especially in an error-prone environment. To make up for resource constraints, the Energy-Aware Genetic Algorithm with Dynamic Resource Allocation (EAGA-DRA) strategy ensures that resources are well-managed by dynamically changing allocation based on the optimization process and the particular needs of the channel, hence reducing energy overhead and spatial overhead without compromising channel quality. A great leap is in the integration of Machine Learning through the Deep Reinforcement Learning-guided Genetic Algorithm (DRL-GA). The above approach exploits deep reinforcement learning to fine-tune the exploration and exploitation strategies of the GA, hence providing faster convergence and improved solution quality. All of these methods are validated by the Quantum-inspired Benchmarking Framework (QIBF), which forms a systematized comparative platform to evaluate different optimization techniques from the perspective of QCA-based communication systems. The above approaches not only provide the improvement in efficiency and reliability but also contribute to enhancing the adaptability of communication systems and provide a very good base for future research and development in the field of quantum communications and beyond, with potential applications extending to secure communication, quantum computing, and nanotechnology-based devices & scenarios. This work is multifold, promising significant advancements in the field of quantum communication and beyond. The empirical findings showed that the suggested algorithms perform significantly better than traditional approaches in several important performance metrics, such as robustness to channel errors, adaptability to changing signal-to-noise ratios, convergence speed under dynamic conditions, and solution quality in highly variable environments.INDEX TERMS Quantum-inspired genetic algorithm, adaptive genetic algorithm, fault-tolerance, energy-efficient optimization, deep reinforcement learning.
Scheduling in quantum networks involves strategically allocating quantum resources to maximize entanglement distribution efficiency and overall performance. Unlike classical networks, quantum systems lack conventional buffering mechanisms, making traditional scheduling paradigms less effective. Existing quantum network algorithms predominantly focus on routing algorithms to identify optimal entanglement paths, often neglecting timeslot scheduling for mitigating resource contention and transmission latency, particularly under multi-user environments. To address these challenges, we propose a routing algorithm tightly integrated with priority-based dynamic scheduling. The algorithm not only discovers efficient entanglement paths but also optimizes resource utilization for concurrent requests. Simulation results confirm that this approach conserves quantum resources, reduces latency, and substantially boosts network performance, providing a robust solution for multi-user quantum environments.
This paper investigates resource allocation for entanglement distribution in multi-node, multi-channel quantum networks at the metropolitan scale. A multi-slot quantum network simulation framework is developed across physical and network layers, incorporating heterogeneous link characteristics, limited quantum memories, request queuing, retry mechanisms, and concurrent entanglement distribution. Based on this framework, a centralized scheduling architecture is proposed to coordinate quantum memories, communication channels, and routing paths for multiple simultaneous entanglement requests. Three classes of resource allocation strategies are evaluated: heuristic approaches, a mixed-integer linear programming (MILP) optimization method, and a Proximal Policy Optimization (PPO)-based reinforcement learning approach. The heuristic schemes reveal fundamental trade-offs between request delay and entanglement success rate: Dynamic Efficient minimizes delay, Success Enhancement improves success probability through adaptive multi-path allocation, and Static Efficient provides a balance between these objectives. The MILP approach achieves optimized resource allocation by jointly considering request handling and multi-path assignment, while the PPO-based method learns adaptive scheduling policies to improve overall performance. Simulation results demonstrate that these approaches provide different performance advantages in terms of request delay, entanglement success rate, capacity utilization, and request handling rate, highlighting the trade-offs between efficiency and reliability in metropolitan-scale quantum network scheduling.
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.