Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing
It addresses dataset imbalance for Intrusion Detection Systems, offering a scalable quantum-based solution that is incremental in applying quantum methods to a known data preprocessing problem.
This study tackled dataset imbalance in Intrusion Detection Systems by implementing a large Quantum Restricted Boltzmann Machine on quantum hardware, which synthesized over 1.6 million attack samples to balance the dataset and significantly improved detection rates, precision, recall, and F1 score compared to traditional methods.
This study explores the implementation of large Quantum Restricted Boltzmann Machines (QRBMs), a key advancement in Quantum Machine Learning (QML), as generative models on D-Wave's Pegasus quantum hardware to address dataset imbalance in Intrusion Detection Systems (IDS). By leveraging Pegasus's enhanced connectivity and computational capabilities, a QRBM with 120 visible and 120 hidden units was successfully embedded, surpassing the limitations of default embedding tools. The QRBM synthesized over 1.6 million attack samples, achieving a balanced dataset of over 4.2 million records. Comparative evaluations with traditional balancing methods, such as SMOTE and RandomOversampler, revealed that QRBMs produced higher-quality synthetic samples, significantly improving detection rates, precision, recall, and F1 score across diverse classifiers. The study underscores the scalability and efficiency of QRBMs, completing balancing tasks in milliseconds. These findings highlight the transformative potential of QML and QRBMs as next-generation tools in data preprocessing, offering robust solutions for complex computational challenges in modern information systems.