Quantum Machine Learning for Electricity Theft Detection: an Initial Investigation

Electricity theft could result in massive economic loss and cause potential security problems to the grids. Therefore, it is of great importance to detect electricity theft from both an economic and social perspective. With the help of Advanced Metering Infrastructure (AMI) in smart grids, now it is possible to detect theft activities through analyzing the recorded electricity consuming data using advanced techniques, such as machine learning. However, training a learning model, especially for the ones with a huge amount of parameters, is always resource-consuming, and users could encounter performance bottlenecks due to hardware limitations. To improve the problem, using quantum computing for machine learning tasks has shown to be an effective approach to speed up the learning process. However, the relevant techniques have not been studied in the domain of energy yet. As a pioneering exploration, in this work, we are trying to leverage a quantum classification algorithm to classify the electricity dataset and on that basis to perform a case study on theft activity detection. Specifically, we have given the details of our design in the study, and our experimental results show that the prediction accuracy can be significantly improved with increasing training epochs, demonstrating the feasibility of quantum machine learning for energy data analysis.