Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 25 Apr 2025.
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16 of 54 references.
References · 16
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4 more
- 2023Improved Financial Forecasting via Quantum Machine Learningcited by 14
- 2018Bayesian Deep Learning on a Quantum Computercited by 12
- 2022Quantum machine learning with subspace statescited by 12
- 2022Bayesian Learning of Parameterised Quantum Circuitscited by 3
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Citing papers per year: 2026: 2.
2 papers.