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.

翻译：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现。如今，密码学家正积极设计后量子密码方案以应对量子赋能的攻击者带来的威胁。与此同时，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有待探索。在本研究中，我们探讨了QML对传统机器学习网络安全应用的潜在影响。首先，我们探究了量子计算在网络安全相关机器学习问题中的潜在优势。随后，我们提出了一种量化容错QML算法对实际问题未来影响的方法论。作为案例研究，我们将该方法应用于网络入侵检测（机器学习在网络安全中最受关注的应用领域之一）的标准方法与数据集。我们的研究结果揭示了获得量子优势所需的条件，以及对未来量子硬件与软件发展的需求。