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arXiv:2411.00316 · IEEE Transactions on Networking

Quantum Entanglement Path Selection and Qubit Allocation via Adversarial Group Neural Bandits

Yin Huang, Lei Wang, Jie Xu

Published Dec 23, 2024• 7 citations
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Abstract

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.

DOI
10.1109/tnet.2024.3510550

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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) typically leverage teleportation to reliably send data quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one entanglement connection between Alice and Bob needs to be established. Accordingly, we have to establish as many entanglement connections as possible with limited quantum resources in order to maximize the network throughput. Conventional methods assume a known traffic matrix and calculate the paths to establish entanglement connections in one batch using a centralized algorithm. However, these methods are not scalable in large scale QDNs since it is time consuming to optimize the entanglement paths for a batch of requests, which may result in a long time slot duration and significantly reduce the network throughput. To address this issue, we propose an Online Entanglement Routing (OER) scheme which determines the entanglement paths for each request when it arrives. In addition, OER pursues work conservation and fairness among all requests in the QDNs. Through extensive simulations, we demonstrate that OER not only outperforms two representative heuristics by up to 61.27% and 52.79%, respectively, in terms of average request completion time, but also achieves a better fairness performance than these two counterparts.
Distributed quantum computing (DQC) holds immense promise in harnessing the potential of quantum computing by interconnecting multiple small quantum computers (QCs) through a quantum data network (QDN). Establishing long-distance quantum entanglement between two QCs for quantum teleportation within the QDN is a critical aspect, and it involves entanglement routing - finding a route between QCs and efficiently allocating qubits along that route. Existing approaches have mainly focused on optimizing entanglement performance for current entanglement connection (EC) requests. However, they often overlook the user's perspective, wherein the user making EC requests operates under a budget constraint over an extended period. Furthermore, both QDN resources (quantum channels and qubits) and the EC requests, reflecting the DQC workload, vary over time. In this paper, we present a novel user-centric entanglement routing problem that spans an extended period to maximize the entanglement success rate while adhering to the user's budget constraint. To address this challenge, we leverage the Lyapunov drift-plus-penalty framework to decompose the long-term optimization problem into per-slot problems, allowing us to find solutions using only the current system information. Subsequently, we develop efficient algorithms based on continuous-relaxation and Gibbs-sampling techniques to solve the per-slot entanglement routing problem. Theoretical performance guarantees are provided for both the per-slot and long-term problems. Extensive simulations demonstrate that our algorithm significantly outperforms baseline approaches in terms of entanglement success rate and budget adherence.
This paper addresses the dual challenges of stabilizing entanglement scheduling and maximizing communication fidelity in multi-hop quantum networks under adversarial dynamics. Unlike prior works relying on stationary assumptions or perfect pre-decision knowledge, we consider scenarios where entanglement generation rates and task arrival patterns vary arbitrarily over time, with only bandit feedback available postscheduling. We first propose Q–NSO, achieving stability for quantum networks under arbitrary time-varying entanglement request arrival rates and bandit feedback. Equipped with the key components of Q-NSO, we propose Q-UMO, a learning-based framework towards maximal utility by determining the entanglement request arrivals and quantum link allocation distribution. Integrating adversarial bandit convex optimization, an online learning algorithm, and Lyapunov drift-plus-penalty analysis, theoretical analysis demonstrates that Q-UMO ensures network stability with bounded queue lengths while achieving a sub-linear regret bound against the reference policy. This work provides a robust foundation for scalable quantum internet applications, balancing stability and performance in adversarial environments with limited feedback.
Efficient entanglement routing in Quantum Data Networks (QDNs) is essential in order to concurrently establish as many Entanglement Connections (ECs) as possible, which in turn maximizes the network throughput. In this work, we consider a new class of QDNs with wavelength division multiplexed (WDM) quantum links where each quantum repeater will perform entanglement swapping by measuring two photonic qubits coming from some entangled photon sources directly on the same wavelength. To address unique challenges in achieving a high network throughput in such QDNs, we propose QuRWA to jointly optimize the entanglement routing and wavelength assignment. To this end, we introduce a key concept named Co-Path to improve fault-tolerance: all ELs in a Co-Path set will be assigned the same wavelength and this may serve as backup for some other ELs in the same Co-Path when establishing ECs. We design efficient algorithms to optimize the Co-Path selection and wavelength assignment to maximize resource utilization and fault tolerance. Extensive simulations demonstrate that compared with the methods without introducing Co-Path, QuRWA improves the network throughput by up to 122%.
In quantum communication networks, the transmission success rate of entangled photons decreases exponentially with the transmission distance in fiber channel. With quantum repeaters, quantum networks can realize arbitrary long-distance links based on quantum entanglement swapping, quantum storage, and quantum teleportation. Thanks to the development of quantum memory technology, the throughput of quantum relay networks can be improved by increasing the number of quantum memories. In this paper, we study the routing metric, the goal of which is to achieve a higher throughput in a long-distance quantum network by reasonably allocating repeater positions with a given memory count between source and destination. A routing metricEEEwas proposed, where the number and distance range of repeater deployments can be obtained by numerical analysis to support an optimal network throughput. According to the above principle, various repeaters were deployed on a linear quantum network to test the effectiveness of the proposed allocation mechanism with the given transmission loss, Bell-State Measurement (BSM) success rate, memory quantity, and distance. The results of evaluation show that increasing the number of repeaters blindly can lead to a waste of resources and a throughput decreasing, which implies that repeaters need to be placed at an appropriate deployment distance to achieve the optimal performance.