Quantum-inspired CNNs show mixed results against classical CNNs in medical imaging

Researchers have compared the performance and explainability of a Hybrid Quantum-inspired Convolutional Neural Network (HQiCNN) against a standard Convolutional Neural Network (CNN) for medical image classification. The study found that neither architecture consistently outperformed the other across all conditions; the HQiCNN showed gains with intermediate data, while the CNN excelled with larger datasets. Removing entanglement from the quantum circuits improved scalability without sacrificing performance, and richer observable sets were only beneficial with sufficient training data. New SHAP-based tools were developed to confirm both models attend to anatomically relevant regions. AI

IMPACT Hybrid quantum-inspired models may offer benefits in specific medical imaging tasks, though classical CNNs remain competitive.

RANK_REASON Academic paper presenting novel research findings. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• CNN
• Guillermo Rubiños Rodríguez
• Hybrid Quantum-inspired Convolutional Neural Network
• medical image classification
• quantum physics
• SHAP

• paper
• other

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