Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection

Understand

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.