QUANTUM IMAGE COMPRESSOR
Quantum Image Compressor (QIC) is an innovative image compression algorithm that leverages hybrid quantum-classical computing to overcome the traditional trade-off between file size and image quality. By encoding image data as quantum state parameters and utilizing quantum compilation algorithms, QIC achieves logarithmic scaling compression efficiency where file size grows only logarithmically with image dimensions. The Fast QIC variant further enhances performance through Taylor expansion optimization and neighbor block analysis, reducing computational iterations by up to 86% while maintaining superior compression quality.
QIC is particularly promising for applications requiring high-efficiency storage and secure image transmission, including cloud computing, medical imaging, and encrypted communications. As quantum computing hardware continues to mature, QIC represents a paradigm shift in image processing technology, offering unprecedented compression ratios and opening new possibilities for quantum-enhanced multimedia applications in the digital era.
KEY FEATURES
1. Logarithmic Scaling Compression: Achieves compression efficiency where file size grows only logarithmically with image dimensions, providing exponential reduction compared to classical methods.
2. Hybrid Quantum-Classical Architecture: Combines quantum compilation algorithms with classical optimization to encode image blocks as quantum state parameters, leveraging the best of both computing paradigms.
3. Taylor Expansion Optimization: Utilizes first-order Taylor expansion to estimate parameters for similar neighboring blocks, significantly reducing computational overhead and iteration requirements.
4. Neighbor Block Analysis: Exploits spatial correlation between adjacent image blocks to minimize redundant quantum evaluations, achieving up to 88% parameter transfer efficiency.
5. Quantum Advantage Scaling: Demonstrates superior performance on high-resolution images, with efficiency gains increasing proportionally to image size rather than degrading like traditional methods.
6. Multi-Encoding Support: Compatible with multiple quantum image representations including FRQI, NEQR, and QHSL for flexible application across different image types and requirements.
7. Integrated Security Framework: Enables seamless integration with quantum encryption systems by compressing images into parameter sets that naturally obscure visual patterns from potential attackers.
8. Resource-Optimized Circuits: Employs efficient Wchain + XYZ ansatz with minimal circuit depth and gate count, making it practical for near-term quantum hardware implementations.