--- license: mit tags: - PyTorch - PennyLane - Quantum - Computer Vision - LPR - Zero-DCE --- # ⚡ Quantum-Enhanced Deep Learning for Robust Night-Time License Plate Recognition Complete architectural pipeline of HybridLPRNet_8Q, illustrating the flow from night-time image input through Zero-DCE enhancement, CNN extraction, the 8-qubit VQC bottleneck, and final Bi-LSTM/CTC decoding. ## 🔭 The Research Question Most license plate recognition (LPR) systems fail after dark. This project asks: **Can a quantum-enhanced neural network read license plates in night conditions more accurately than a purely classical system?** We run two systems head-to-head: - `HybridLPRNet_8Q`: Zero-DCE + CNN + **8-Qubit Quantum Circuit** + Bi-LSTM + CTC - `ClassicalLPRNet`: Zero-DCE + CNN (larger) + Bi-LSTM + CTC Representative night-time ALPR examples showing original image, synthetic night corruption, Zero-DCE enhancement, and predictions from both models. --- ## 📊 Performance Comparison & Convergence The quantum model achieves competitive performance utilizing a dynamic 256-dimensional Hilbert space, requiring **fewer overall parameters** compared to the classical baseline. Comparative training convergence plots for the Quantum (HybridLPRNet_8Q) and Classical (ClassicalLPRNet) models showing Validation Loss, CER, and Plate Accuracy over 100 epochs. | Metric | ⚡ Quantum Model (`HybridLPRNet_8Q`) | 🔷 Classical Baseline (`ClassicalLPRNet`) | |---|---|---| | **Parameters** | **~1.2M** | ~1.8M | | **GPU VRAM Usage** | **~1.7 GiB** | ~1.7 GiB | | **Best Val CER** | 1.586% | 1.330% | | **Plate Accuracy** | 92.0% | 92.5% | | **Training Platform** | Kaggle T4 x2 (cuda:0 constraint) | Kaggle T4 x2 | --- ## 🏗️ Architecture Deep-Dive ### 1. Low-Light Enhancement (Zero-DCE) Stage 1: Detailed view of the Zero-DCE (Deep Curve Estimation) light module, showing the 8-iteration curve refinement process for low-light enhancement. ### 2. Quantum Bottleneck (VQC) Stage 4: Schematic of the 8-qubit Variational Quantum Circuit (VQC) featuring AngleEmbedding and two StronglyEntanglingLayers. --- ## 🔮 Quantum Interpretability The 8-qubit register exhibits emergent specialization during training. Different qubits learn to react to specific visual features, such as numeral density or character boundaries. Pre-circuit and post-circuit qubit activation maps across temporal slices for a representative license plate. Bloch-sphere state trajectories for the 8 qubits, showing specialized rotational behavior. --- ## 💻 Reproduce the Research All execution environments and training code are fully available in the `notebooks/` directory. ### Inference Example ```python import torch # Load the Quantum Checkpoint checkpoint = torch.load("quantum/latest.pth", map_location="cpu") print(f"Loaded Epoch: {checkpoint.get('epoch', 'N/A')}") print(f"Validation CER: {checkpoint.get('val_cer', 'N/A')}") # Assuming HybridLPRNet_8Q is defined in your scope: # model = HybridLPRNet_8Q() # model.load_state_dict(checkpoint['model_state_dict']) # model.eval() ``` --- ## 👤 Author **Shanmukesh Bonala** — VIT-AP University Course: CSE4019 — Applications of AI (AoAI) Semester: Winter 2025–26
