As an emerging technology, quantum networks have the potential to revolutionize secure communication and data transmission technologies. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise causing low fidelity remains challenging in quantum networks. In this paper, we propose QLSel, an efficient selection algorithm for the high-fidelity link in the wild without assumptions about the fidelity distribution. We design the tailored link exploration strategy and link selection probability based on the coefficient of variation and Thompson sampling, to cope with the exploration-exploitation trade-off dilemma in the Multi-Armed Bandit (MAB) problem (for problem modeling). Extensive experiments demonstrate that QLSel significantly outperforms existing representative methods. Our codes and video are available at https://github.com/Secbrain/QLSel.
As an emerging technology, quantum networks have the potential to revolutionize secure communication and data transmission technologies. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise causing low fidelity remains challenging in quantum networks. In this paper, we propose QLSel, an efficient selection algorithm for the high-fidelity link in the wild without assumptions about the fidelity distribution. We design the tailored link exploration strategy and link selection probability based on the coefficient of variation and Thompson sampling, to cope with the exploration-exploitation trade-off dilemma in the Multi-Armed Bandit (MAB) problem (for problem modeling). Extensive experiments demonstrate that QLSel significantly outperforms existing representative methods. Our codes and video are available at https://github.com/Secbrain/QLSel.
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.
Quantum networks represent the forefront of modern communication systems, offering promising advantages over classical communication. They leverage the unique properties of quantum mechanics, such as superposition and entanglement, to enable tasks impossible in classical computing. However, the current era of Noisy Intermediate-Scale Quantum (NISQ) devices presents significant challenges due to the susceptibility of quantum systems to noise, leading to errors and decoherence that degrade the quality of quantum communication. To measure quantum link fidelity, quantum network benchmarking technology repeatedly assesses the fidelity of each link and averages the measurement results. Existing measurement schemes suffer from a trade-off between measurement overhead and accuracy. To this end, we propose QuFM, which is an adaptive scheme that leverages Kalman filter modeling to determine whether the stable state of Quantum State Tomography is reached when performing fidelity measurement, thereby achieving early termination of iteration to reduce the number of measurements. Through extensive experiments, we demonstrate the effectiveness of QuFM in achieving accurate and efficient fidelity measurements, and its applicability to different error types and network topologies.
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.
Finding a path with the least overall noise from quantum memories, fibers, and gate operations in a quantum network involves the challenge of acquiring knowledge of these noises, their sources, and the time of occurrences. In this paper, we propose a reinforcement learning-based route selection approach that uses a multi-arm bandit algorithm to find the least noisy path from a transmitter (Tx) to a receiver (Rx), without considering any information on qubit decoherence due to probabilistic noises inherent in quantum memories and imperfect gate operations. It only uses network deployment knowledge to find a set of feasible paths from Tx to Rx. We provide a key finding from a network design perspective which says that performing entanglement swapping on nodes within a path in a non-synchronized and parallel manner not always reduces the decoherence experienced in achieving the end-to-end entanglement on that path. Further, we design and open-source a new simulator for simulating probabilistic noises encountered during entanglement distribution between Tx-Rx on a path, which has supporting callable functions for connecting the unknown network environment required for interaction with the multi-arm bandit agent. The simulation results demonstrate that our proposed route selection approach provides a path up to ~ 33% better fidelity (less noise) compared to conventional, distance-based route selection approach for the considered quantum network.
Quantum Data Networks (QDNs) have emerged as a promising framework in the field of information processing and transmission, harnessing the principles of quantum mechanics. QDNs utilize a quantum teleportation technique through long-distance entanglement connections, encoding data information in quantum bits (qubits). Despite being a cornerstone in various quantum applications, quantum entanglement encounters challenges in establishing connections over extended distances due to probabilistic processes influenced by factors like optical fiber losses. The creation of long-distance entanglement connections between quantum computers involves multiple entanglement links and entanglement swapping techniques through successive quantum nodes, including quantum computers and quantum repeaters, necessitating optimal path selection and qubit allocation. Current research predominantly assumes known success rates of entanglement links between neighboring quantum nodes and overlooks potential network attackers. This paper addresses the online challenge of optimal path selection and qubit allocation, aiming to learn the best strategy for achieving the highest success rate of entanglement connections between two chosen quantum computers without prior knowledge of the success rate and in the presence of a QDN attacker. The proposed approach is based on multi-armed bandits, specifically adversarial group neural bandits, which treat each path as a group and view qubit allocation as arm selection. Our contributions encompass formulating an online adversarial optimization problem, introducing the EXPNeuralUCB bandits algorithm with theoretical performance guarantees, and conducting comprehensive simulations to showcase its superiority over established advanced algorithms.
Quantum multi-armed bandits (MAB) and stochastic linear bandits (SLB) have recently attracted significant attention, as their quantum counterparts can achieve quadratic speedups over classical MAB and SLB. However, most existing quantum MAB algorithms assume ideal quantum Monte Carlo (QMC) procedures on noise-free circuits, overlooking the impact of noise in current noisy intermediate-scale quantum (NISQ) devices. In this paper, we study a noise-robust QMC algorithm that improves estimation accuracy when querying quantum reward oracles. Building on this estimator, we propose noise-robust QMAB and QSLB algorithms that enhance performance in noisy environments while preserving the advantage over classical methods. Experiments show that our noise-robust approach improves QMAB estimation accuracy and reduces regret under several quantum noise models.