标题： 评估量子机器学习在网络安全中的潜力：基于PCA的入侵检测系统的案例研究 显示英文标题

标题： Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems

摘要： 量子计算有望彻底改变我们对计算极限的理解，其在密码学中的影响早已显而易见。 如今，密码学家正在积极设计后量子解决方案，以应对量子能力对手带来的威胁。 同时，量子科学家正在创新量子协议，以增强防御者的能力。 然而，量子计算和量子机器学习（QML）对其他网络安全领域的更广泛影响仍需进一步探索。 在本工作中，我们研究了QML对传统机器学习网络安全应用的潜在影响。 首先，我们探讨了量子计算在与网络安全相关的机器学习问题中的潜在优势。 然后，我们描述了一种方法，用于量化容错QML算法对未来实际问题的影响。 作为案例研究，我们将我们的方法应用于网络入侵检测中的标准方法和数据集，这是机器学习在网络安全中最受研究的应用之一。 我们的结果提供了关于获得量子优势的条件以及未来量子硬件和软件进步的必要性的见解。 显示英文摘要

摘要： Quantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are actively devising post-quantum solutions to counter the threats posed by quantum-enabled adversaries. Meanwhile, quantum scientists are innovating quantum protocols to empower defenders. However, the broader impact of quantum computing and quantum machine learning (QML) on other cybersecurity domains still needs to be explored. In this work, we investigate the potential impact of QML on cybersecurity applications of traditional ML. First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity. Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems. As a case study, we apply our approach to standard methods and datasets in network intrusion detection, one of the most studied applications of machine learning in cybersecurity. Our results provide insight into the conditions for obtaining a quantum advantage and the need for future quantum hardware and software advancements.