QuantumNet is investigating how Quantum Convolutional Neural Networks (QCNNs) can pinpoint road surface anomalies like cracks and potholes, starting with detailed segmentation masks. The Naples-based startup presented this research at the IEEE International Conference on Intelligent Transportation Systems (ITSC) 2026, held from September 15 to 18. By applying quantum machine learning to computer vision, QuantumNet aims to offer new tools for road infrastructure monitoring and maintenance. The company stated.
QCNNs for Road Surface Anomaly Mask Classification
The approach moves beyond simple damage detection by beginning with segmentation masks, allowing for precise differentiation between anomalies like cracks and potholes. This focus on image-based precision suggests a move toward detailed infrastructure monitoring, rather than broad assessments of road condition. The company’s presentation on September 17, outlined in the paper “Quantum Convolutional Neural Networks for Road Surface Anomaly Mask Classification,” explores how using quantum-enhanced architectures can classify images relevant to road maintenance.
QuantumNet’s work intersects quantum machine learning with computer vision, a combination not widely discussed despite its potential for improving infrastructure management. This research reflects QuantumNet’s broader aim of applying quantum computing to real-world transportation challenges where efficient anomaly detection is important for safety and planning.
Founded in 2021 as a collaboration between NetCom Group and the University of Naples Federico II, QuantumNet is Italy’s first innovative startup specializing in quantum computing. The company’s expertise spans big data, IoT, optimisation, quantum machine learning, and cybersecurity, alongside its Quantum Computing Academy, which provides training in the field, QuantumNet says. Presenting at ITSC 2026 provided a platform for direct engagement with the international research community, allowing the team to gather feedback that will inform future development of the QCNN architecture and potential applications within the transportation domain.
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