Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing

TL;DR

This paper demonstrates the successful implementation of large Quantum Restricted Boltzmann Machines on quantum hardware to generate high-quality synthetic data, significantly improving dataset balancing for intrusion detection systems.

Contribution

It introduces a scalable method for implementing large QRBMs on Pegasus hardware, surpassing previous limitations and enhancing data balancing in cybersecurity applications.

Findings

QRBMs generated over 1.6 million attack samples

Balanced datasets improved detection metrics

QRBMs completed balancing in milliseconds

Abstract

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.