Quantum-Assisted Variational Deep Learning for Efficient Anomaly Detection in Secure Cyber-Physical System Infrastructures
Nilesh Bhosale, Bukya Mohan Babu, M. Karthick Raja, Yousef A. Baker El–Ebiary, Manasa Adusumilli, Elangovan Muniyandy, David Neels Ponkumar Devadhas · International Journal of Advanced Computer Science and Applications · 2025
The aim of the current study is to propose a Quantum-Assisted Variational Autoencoder (QAVAE) model capable of efficiently identifying anomalies in high-dimensional, time-series data produced by cyber-physical systems. The existing approaches to machine learning have some limitations when recording temporal interactions and take substantial time to run with many attributes. To meet all of these challenges, this study aims at proposing a quantum-assisted approach to anomaly detection using the potential of a Quantum-Assisted Variational Autoencoder (QAVAE). The general goal of this research is to optimize anomaly detection systems using consummate deep learning quantum computing models. According to the QAVAE framework, variational inference is employed for learning latent representations of time series data; besides, quantum circuits are utilized for enhancing the capacity of the model and its generalization capability. This work was accomplished using Python programming language, and the analysis was carried out using TensorFlow Quantum. The QAVAE model demonstrates the highest accuracy of 95.2%, indicating its strong capability in correctly identifying both anomalous and normal instances. So, it can learn well from the data and keep stable in the evaluation process, which will make it suitable for real-time anomaly detection in dynamic environments. In conclusion, the QAVAE model brings a reasonable approach and solution for anomaly detection that is accurate in identifying and scalable too. Utilizing the HAI, the dataset achieved a high detection accuracy of 95.2%. Further research has to be dedicated to its application to quantum computing architecture as well as to modifications that allow for its use on multi-variable actual-life data.