用于异常检测的贝叶斯量子正交神经网络
Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
AI总结：
本文提出贝叶斯量子正交神经网络，结合贝叶斯学习与正交性，用于三维物体异常检测，并在IBM量子设备上验证了可行性。
摘要：
识别三维物体中的缺陷或异常是确保其正常功能的关键任务。在这项工作中，我们将贝叶斯学习与量子及量子启发机器学习的最新进展（特别是正交神经网络）相结合，以解决一个工业相关用例中的异常检测问题。贝叶斯学习能够对预测进行不确定性量化，而权重矩阵的正交性则有助于平滑训练。我们开发了三维卷积神经网络的正交（量子）版本，并证明这些模型能够成功检测三维物体中的异常。为了测试将量子计算机纳入量子增强异常检测流程的可行性，我们在IBM的127量子比特Brisbane设备上使用我们的模型进行了硬件实验，测试了噪声和有限测量次数的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
