With the explosion of data over the past decades there has been a respective explosion of techniques to extract information from the data from labeled data, quasi-labeled data, and data with no labels known a priori. For data with at best quasi-labels, graphs are a natural structure to connect points to further extract information. In particular, anomaly detection in graphs is a method to determine which data points do not posses the latent characteristics of the other data. There have been a variety of classical methods to score vertices on their anomalous level with respect to the graph, spanning straightforward methods of checking the local topology of a node to intricate neural networks. Leveraging the structure of the graph, we propose a first ever quantum-based technique to calculate the anomaly score of each node by continuously traversing the graph in a particular manner. The proposed algorithm incorporates well-known characteristics of quantum random walks, and an adjustment to the algorithm is given to mitigate the increasing depth of the circuit. This algorithm is rigorously shown to converge to the expected probability, with respect to the initial condition.
