Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing
This study implemented large Quantum Restricted Boltzmann Machines (QRBMs) on D-Wave's Pegasus quantum hardware to balance imbalanced datasets in Intrusion Detection Systems (IDS). The QRBM generated over 1.6 million attack samples, creating a balanced dataset of 4.2 million records. QRBMs outperformed classical methods like SMOTE by producing higher-quality synthetic data, significantly improving detection rates and precision across various classifiers.