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.

The computing continuum combines computational resources and services from edge to cloud, promising enhanced efficiency and resilience with respect to the traditional siloed-based approach. This study presents the REAR (Resource Advertisement and Reservation) protocol, which tackles the complexities of managing resources within this continuum. REAR establishes standardized interfaces to enable interoperability, enhances resource allocation efficiency, and maintains security measures for workload execution. The paper details the protocol’s design, key components, operational workflows, and potential uses, contributing to the optimization of resource use across the computing continuum.