Develop a hybrid quantum-classical deep learning system for ...

Prompt

Develop a hybrid quantum-classical deep learning system for the detection and classification of major pulmonary diseases, including Pneumonia, Tuberculosis (TB), and COVID-19, using chest X-ray and Computed Tomography (CT) scan images. The system uses ResNet50 (Residual Network-50) as the primary classical feature-extraction backbone, combined with a quantum machine learning (QML) component to explore whether quantum-enhanced processing can improve feature representation, classification accuracy, or computational efficiency over purely classical approaches. 1. Classical Backbone Use ResNet50, pretrained via transfer learning on ImageNet, as the deep feature extractor. Retain its residual connections to capture complex visual patterns (opacities, consolidations, ground-glass textures, cavitations) from chest X-ray/CT images. Extract the 2048-dimensional feature vector from the global average pooling layer as the standard baseline pipeline output. 2. Quantum Machine Learning Layer Introduce a quantum-enhanced classification stage that receives the ResNet50 feature vector and processes it through a hybrid quantum-classical model: Dimensionality reduction — Apply PCA or an autoencoder to compress the 2048-dim ResNet50 features down to a small number of qubits (e.g., 4–10), since near-term quantum hardware/simulators can only handle limited qubit counts. Quantum feature encoding — Map the reduced classical features into quantum states using an encoding scheme such as angle encoding or amplitude encoding. Variational Quantum Classifier (VQC) — Use a parameterized quantum circuit (PQC) with trainable rotation gates and entangling layers to process the encoded features, optimized alongside the classical network using a hybrid quantum-classical training loop (e.g., via PennyLane or Qiskit Machine Learning integrated with PyTorch/TensorFlow). Quantum Convolutional Neural Network (QCNN) as an optional alternative architecture, applying quantum convolution and pooling operations analogous to classical CNNs. Output layer produces class probabilities for Normal, Pneumonia, TB, and COVID-19. 3. Workflow Data collection from publicly available labeled chest X-ray/CT datasets Preprocessing: resizing, normalization, contrast enhancement, augmentation Dataset splitting (train/validation/test) Classical feature extraction via ResNet50 Feature compression for quantum compatibility Quantum circuit training (hybrid quantum-classical backpropagation) Model validation and testing Final disease classification with confidence scores 4. Comparative Evaluation Benchmark the hybrid quantum-classical model against: Classical ResNet50 with a standard dense/softmax classifier Traditional ML approaches (SVM, Random Forest) on extracted features Conventional CNN architectures (VGG, DenseNet, EfficientNet) Evaluation metrics: accuracy, precision, recall, F1-score, sensitivity, specificity, confusion matrix, AUC-ROC, training/inference time, and quantum-specific metrics such as qubit count, circuit depth, and simulator vs. real-hardware performance gap. 5. Visualization & Explainability Training/validation accuracy and loss curves for both classical and quantum-hybrid models Confusion matrices for each model variant Grad-CAM (on the classical ResNet50 branch) to highlight lung regions influencing predictions Quantum circuit visualization (circuit diagrams, parameter convergence plots) to interpret the learned quantum layer Side-by-side comparison charts of classical vs. quantum-hybrid performance 6. Objective The primary objective is to investigate whether integrating quantum machine learning components with a proven classical architecture (ResNet50) offers measurable advantages — in accuracy, generalization, or computational trade-offs — for pulmonary disease screening from medical images. The system is intended as a research and decision-support prototype exploring quantum-AI feasibility in medical imaging, and not a replacement for professional medical diagnosis or a claim of near-term quantum advantage, given current hardware (NISQ-era) limitations.