The advent of quantum computers has justified the development of quantum machine learning algorithms, based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The survey provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The survey provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.

中文翻译：

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用于异常情况检测的量子机器学习算法：综述


量子计算机的出现证明了量子机器学习算法的发展是合理的，该算法基于机器学习原理对量子比特形式主义的适应。在此类量子算法中，异常检测是一个跨越多个学科的重要问题，从网络安全到欺诈检测再到粒子物理学。我们总结了量子计算中涉及的关键概念，介绍了量子加速的正式概念。该调查提供了基于量子机器学习的异常检测结构化地图。我们根据不同的学习方法对现有算法进行了分组，分别是量子监督学习、量子无监督学习和量子强化学习。我们提供了硬件资源的估计值，以便在将来提供足够的计算能力。该调查提供了对属于每个类别的技术的系统和紧凑的理解。我们最终讨论了实际应用领域中学习方法的计算复杂性。