用贝叶斯正交神经网络检测3D物体异常，兼顾不确定性与训练稳定性。
Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
- 结合贝叶斯学习与正交权重的3D卷积网络，提升预测可信度。
- 在IBM 127量子机上实测，噪声与采样有限下仍能有效检测异常。
- 适合工业缺陷检测场景，尤其关注模型可靠性与可部署性。
3D物体中的缺陷识别对确保功能正确至关重要。本文将贝叶斯学习与近期量子及量子启发机器学习中的正交神经网络相结合，解决一个具有工业应用价值的异常检测问题。贝叶斯学习实现预测不确定性量化，而权重矩阵的正交性促进平稳训练。我们开发了3D卷积神经网络的正交（量子）版本，并证明其能成功检测3D物体中的异常。为验证将量子计算机融入量子增强异常检测流程的可行性，我们在IBM 127量子比特的Brisbane设备上进行了硬件实验，测试了噪声和有限测量次数的影响。
原文摘要 · Abstract (English)
Identification of defects or anomalies in 3D objects is a crucial task to ensure correct functionality. In this work, we combine Bayesian learning with recent developments in quantum and quantum-inspired machine learning, specifically orthogonal neural networks, to tackle this anomaly detection problem for an industrially relevant use case. Bayesian learning enables uncertainty quantification of predictions, while orthogonality in weight matrices enables smooth training. We develop orthogonal (quantum) versions of 3D convolutional neural networks and show that these models can successfully detect anomalies in 3D objects. To test the feasibility of incorporating quantum computers into a quantum-enhanced anomaly detection pipeline, we perform hardware experiments with our models on IBM's 127-qubit Brisbane device, testing the effect of noise and limited measurement shots.
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