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Opto-Electronics and Communications Conference (OECC)

Quantum attack-resistant resource allocation using reinforcement learning in CV-QKD over optical networks

Shifeng Ding, Yuansen Cheng, Calvin Chun-Kit Chan

Published Jun 30, 2024• 1 citation
Publisher

Abstract

We propose a quantum attack-resistant resource allocation using reinforcement learning in CV-QKD over optical networks. Simulation results show that it can effectively mitigate quantum attacks and reduce the request blocking probability.
DOI
10.1109/oecc54135.2024.10975570

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Due to possible imperfect implementation of laser sources and coherent detectors, continuous-variable quantum key distribution (CV-QKD) in optical networks is vulnerable to quantum attacks, which would significantly degrade the secret key rate (SKR) and compromise the security of the quantum keys. The attackers may steal the data encrypted with the compromised keys, leading to service failure and resource wastage. To tackle it, we propose a quantum attack mitigation resource allocation scheme (QAM-RA) for CV-QKD over optical networks. It corrects the biased channel parameters of the compromised quantum channels by conducting appropriate countermeasures, re-analyzes the SKR, and allocates additional quantum resources to compensate for the loss of SKR. A deep reinforcement learning (DRL) framework based on the Asynchronous Advantage Actor-Critic (A3C) algorithm is developed to determine QAM-RA solutions intelligently. Extensive simulations have been conducted to evaluate the performance of the DRL-assisted QAM-RA scheme in two test networks. Simulation results have confirmed the effectiveness of the proposed scheme in mitigating quantum attacks and reducing user request blocking probabilities.
Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.
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.