本文研究了利用D-Wave Pegasus量子硬件实施大型量子受限玻尔兹曼机（QRBMs）作为生成模型，以解决入侵检测系统（IDS）中的数据不平衡问题。通过Pegasus的高连通性和计算能力，成功嵌入一个拥有120个可见和120个隐藏单元的QRBM，并超越了默认嵌入工具的限制。QRBM合成了超过160万个攻击样本，实现了一个超过420万条记录的平衡数据集。与传统平衡方法（如SMOTE和RandomOversampler）的对比评估表明，QRBMs产生的合成样本质量更高，显著提高了检测率、精确度、召回率和F1分数。该研究强调了QRBMs在数据预处理中的可扩展性和效率，以及作为下一代工具在信息系统中解决复杂计算挑战的潜力。

arXiv:2502.03086v2 Announce Type: replace-cross 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.