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Shanmuk4622/
hybrid-quantum-classical-LPR

Name: hybrid-quantum-classical-LPR
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⚡ 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

Shanmuk4622/hybrid-quantum-classical-LPR