NQQR: Scalable Qutrit Image Representation using Cirq (Monte Carlo Reconstruction under Quantum Depolarizing Noise)
Year: 2026
Type: software
Abstract: This repository provides the Cirq-based implementation of the proposed NQQR (Scalable Qutrit Image Representation) framework for quantum image processing. The implementation demonstrates a qutrit-based image encoding and reconstruction approach under quantum depolarizing noise using Google Cirq simulator. It includes Jupyter notebooks for grayscale and RGB image reconstruction, enabling reproducible experiments and clear visualization of the proposed framework. The experimental workflow consists of: - Encoding classical images using the NQQR model - Injecting quantum depolarizing noise into the encoded qutrit states - Reconstructing images using Monte Carlo sampling - Applying statistical estimation (mean and median) for robust reconstruction - Evaluating performance using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) The proposed statistical reconstruction approach enhances noise resilience without relying on quantum error correction techniques. The implementation supports different image sizes (3×3, 9×9, 27×27, and beyond) and varying noise levels. This repository is intended to ensure reproducibility, facilitate independent validation, and support further research in qutrit-based quantum image processing under realistic noise conditions.
Authors: Rofail, M, Montaser, R, Younes, A
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162190829