Automated and reproducible benchmarking framework for quantum computing workflows.
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Updated
Feb 12, 2026 - Python
Automated and reproducible benchmarking framework for quantum computing workflows.
Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer and includes a Gradio-based prediction interface.
Hybrid Quantum–Classical model for brain tumor classification using Quantum FiLM modulation and ResNet-18. Supports multi-class MRI tumor detection with quantum circuit integration.
Proof-of-concept hybrid quantum-classical neural network classifier on make_moons using Qiskit EstimatorQNN + PyTorch TorchConnector. Achieves 100% test accuracy
🧠 Classify brain tumors using a hybrid QCNN with ResNet for accurate MRI image analysis across multiple categories, including no tumor detection.
🧠 Detect brain tumors using a hybrid Quantum + Classical model with MRI images, enhancing accuracy and efficiency in diagnosis through advanced AI.
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