Accurate selection of high-fidelity quantum links is essential for ensuring reliable quantum communication over noisy networks. However, the fidelity of each link is typically unknown and must be estimated through repeated quantum benchmarking, which can incur substantial resource costs—especially in networks with multiple redundant paths. To address this challenge, we formulate the link selection and fidelity estimation task as a best-arm identification problem under the reinforcement learning framework. Based on this formulation, we propose RL-QLiS, a lightweight and adaptive link selection algorithm that efficiently allocates quantum benchmarking resources to promising links while periodically exploring others. RL-QLiS dynamically refines fidelity estimates using feedback from benchmarking subroutines and effectively balances exploration and exploitation through a Q-learning-based strategy. Unlike exhaustive methods, our approach minimizes redundant evaluations and focuses on high-potential links. Extensive simulations demonstrate that RL-QLiS consistently identifies the optimal entangled link while significantly reducing quantum resource consumption compared to existing baselines.
Accurate selection of high-fidelity quantum links is essential for ensuring reliable quantum communication over noisy networks. However, the fidelity of each link is typically unknown and must be estimated through repeated quantum benchmarking, which can incur substantial resource costs—especially in networks with multiple redundant paths. To address this challenge, we formulate the link selection and fidelity estimation task as a best-arm identification problem under the reinforcement learning framework. Based on this formulation, we propose RL-QLiS, a lightweight and adaptive link selection algorithm that efficiently allocates quantum benchmarking resources to promising links while periodically exploring others. RL-QLiS dynamically refines fidelity estimates using feedback from benchmarking subroutines and effectively balances exploration and exploitation through a Q-learning-based strategy. Unlike exhaustive methods, our approach minimizes redundant evaluations and focuses on high-potential links. Extensive simulations demonstrate that RL-QLiS consistently identifies the optimal entangled link while significantly reducing quantum resource consumption compared to existing baselines.
Reliable transmission of fragile quantum information requires one to efficiently select and utilize high-fidelity links among multiple noisy quantum links. However, the fidelity, a quality metric of quantum links, is unknown a priori. Uniformly estimating the fidelity of all links can be expensive, especially in networks with numerous links. To address this challenge, we formulate the link selection and fidelity estimation problem as a best arm identification problem and propose an algorithm named LinkSelFiE. The algorithm efficiently identifies the optimal link from a set of quantum links and provides an accurate fidelity estimate of that link with low quantum resource consumption. LinkSelFiE estimates link fidelity based on the feedback of a vanilla network benchmarking subroutine, and adaptively eliminates inferior links throughout the whole fidelity estimation process. This elimination leverages a novel confidence interval derived in this paper for the estimates from the subroutine, which theoretically guarantees that LinkSelFiE outputs the optimal link correctly with high confidence. We also establish a provable upper bound of cost complexity for LinkSelFiE. Moreover, we perform extensive simulations under various scenarios to corroborate that LinkSelFiE outperforms other existing methods in terms of both identifying the optimal link and reducing quantum resource consumption.
This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
Quantum entanglement brings high-speed and inherently privacy-preserving transmission for information communication in quantum networks. The qubit scarcity is an important issue that cannot be ignored in quantum networks due to the limited storage capacity of quantum devices, the short lifespan of qubits, and so on. In this article, we first formulate the qubit competition problem as the Cooperative-Qubit-Allocation-Problem (CQAP) by taking into account both the waiting time and the fidelity of end-to-end entanglement with the given transmission link set. We then model the CQAP as a Markov Decision Process (MDP) and adopt a Reinforcement Learning (RL) algorithm to self-adaptively and cooperatively allocate qubits among quantum repeaters. Further, we introduce an Active Learning (AL) algorithm to improve the efficiency of the RL algorithm by reducing its trial-error times. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithms, with 23.5 ms reduction on the average waiting time and 19.2 improvement on the average path maturity degree, respectively.
We propose an adaptive quantum routing protocol designed for dynamic network topologies in which link fidelity and availability vary stochastically over time. By introducing a novel Link Stability Metric that integrates predictive fidelity estimation with historical reliability analysis, the protocol can make real-time routing decisions that balance communication quality, latency and quantum resource consumption. The routing problem is formulated as a Markov Decision Process and solved using a deep reinforcement learning (RL) framework, enabling intelligent entanglement path selection and adaptive switching in response to channel degradation. We derive a fundamental bound on achievable end-to-end fidelity in dynamic quantum networks and provide convergence guarantees for the learning-based policy. Extensive simulations on scale-free and random geometric networks demonstrate significant improvements in success probability, fidelity and latency compared with state-of-the-art benchmarks, validating the theoretical analysis and showing the potential of adaptive learning approaches for robust, scalable quantum internetworking.
This paper investigates the application of quantum-inspired algorithms to the field of reinforcement learning (RL). Traditional RL methods often struggle with exploration challenges and can be computationally expensive, particularly in complex environments. We propose a novel RL algorithm that leverages concepts from quantum mechanics, specifically superposition and entanglement, to address these limitations. The core idea is to represent states, actions, and rewards using quantum-inspired representations, allowing the agent to explore the environment more efficiently and potentially converge to optimal policies faster. This work presents a detailed formulation of the algorithm, including the mathematical framework and key considerations for implementation. The potential benefits of this approach, including improved exploration and reduced computational complexity, are discussed. The algorithm is designed to iteratively refine its policy based on simulated quantum measurements, aiming to minimize the expected return. The exploration strategy is guided by the inherent probabilistic nature of quantum states, leading to a more diverse and potentially faster learning process. Further research is warranted to fully explore the capabilities and limitations of this approach across various RL benchmark problems.
This paper addresses the critical challenge of optimizing resource allocation within dynamic quantum networks (QNs). The inherent complexities of QNs, including fluctuating demands, evolving topologies, and the fragility of quantum states, necessitate adaptive resource management strategies. We propose a novel algorithm leveraging reinforcement learning (RL) to dynamically adjust resource allocation based on real-time network conditions. The algorithm learns an optimal policy by iteratively interacting with a simulated QN environment, maximizing the overall quantum channel throughput while minimizing the probability of quantum error. Specifically, the core mechanism involves formulating the resource allocation problem as a Markov Decision Process (MDP), where the state represents the network's current configuration, the action represents the allocation of quantum resources (e.g., qubits, entanglement), and the reward reflects the successful completion of quantum communication tasks. The RL agent utilizes a Q-learning algorithm to estimate the optimal Q-function, guiding the allocation process. We demonstrate, through simulation, that our algorithm significantly outperforms static allocation strategies in terms of throughput and reliability, even under highly dynamic network conditions. The key contributions of this work lie in the integration of RL with the unique characteristics of QNs and the development of a practical algorithm for adaptive resource management. Further research will explore the scalability of this approach and its application to larger, more complex QN topologies.