Partial adversarial coverage in simulated quantum federated learning improves small-perturbation robustness with little clean-accuracy loss, but label-sorted non-IID data removes about half the robustness benefit.
Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks
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Accurate classification of brain tumors from MRI scans is critical for effective treatment planning. This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circuits with depth-wise separable convolutional layers to analyze images from a publicly available brain tumor dataset. Evaluated on this dataset, the HQCNN achieved 99.16% training accuracy and 91.47% validation accuracy, demonstrating robust performance across varied imaging conditions. The quantum layers capture complex, non-linear relationships, while separable convolutions ensure computational efficiency. By reducing both parameter count and circuit depth, the architecture is compatible with near-term quantum hardware and resource-constrained clinical environments. These results establish a foundation for integrating quantum-enhanced models into medical-imaging workflows with minimal changes to existing software platforms. Future work will extend evaluation to multi-center cohorts, assess real-time inference on quantum simulators and hardware, and explore integration with surgical-planning systems.
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RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Partial adversarial coverage in simulated quantum federated learning improves small-perturbation robustness with little clean-accuracy loss, but label-sorted non-IID data removes about half the robustness benefit.