Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
This addresses defect identification for industrial quality control, but it is incremental as it adapts existing quantum and Bayesian methods to a specific domain.
The paper tackles anomaly detection in 3D objects by combining Bayesian learning with quantum-inspired orthogonal neural networks, achieving successful detection and testing on IBM's 127-qubit quantum hardware to assess noise and measurement effects.
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.