NQQR: scalable qutrit image representation.

Area of Science:

• Quantum Computing
• Image Processing
• Quantum Information Science

Background:

• Ternary quantum image processing (TQIP) leverages qutrit-based quantum computing for advanced image representation.
• Developing efficient quantum encoding models is crucial for advancing TQIP.

Purpose of the Study:

• Introduce a Novel Qutrit Quantum Representation (NQQR) model for scalable and optimized ternary image encoding.
• Evaluate the efficiency and robustness of the NQQR model for grayscale and RGB images.

Main Methods:

• The NQQR model encodes pixel positions using qutrit superposition and intensity values using dedicated qutrits.
• Quantum cost reduction is achieved through strategic use of ancilla and color qutrits to simplify multi-controlled gates.
• Model performance is assessed via time complexity analysis and evaluation under quantum depolarizing noise using MSE and PSNR metrics.

Main Results:

• The NQQR model achieves a time complexity of O(n).
• Optimized NQQR demonstrates significant quantum cost reductions, up to 90% for grayscale and 80% for RGB images.
• The model shows efficiency and scalability, maintaining performance under varying noise levels.

Conclusions:

• The NQQR model offers an efficient and scalable approach for ternary quantum image representation.
• The proposed model serves as a foundational component for future TQIP research.
• NQQR demonstrates robustness against quantum noise, highlighting its practical potential.