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An improved novel quantum image representation and its experimental test on IBM quantum experience
Jie Su1, Xuchao Guo2, Chengqi Liu2
1College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China. sujiework@cau.edu.cn.
Scientific Reports
|July 7, 2021
Summary
An improved quantum image representation (INCQI) model enhances quantum image processing by enabling multi-channel and color image handling, overcoming limitations of previous methods. Experiments on IBM Quantum Experience confirm its effectiveness in preparing and visualizing quantum image data.
Area of Science:
- Quantum Information Science
- Quantum Computing
- Image Processing
Background:
- Quantum image representation (QIR) is fundamental to quantum image processing (QIP).
- Existing methods like the No-Cloning Quantum Quantization (NCQI) model face limitations with multi-channel and inconsistently sized images.
- Addressing these limitations is crucial for advancing QIP applications.
Purpose of the Study:
- To propose an improved color digital image quantum representation (INCQI) model.
- To overcome the limitations of NCQI in handling multi-channel and inconsistently sized images.
- To demonstrate the feasibility and effectiveness of the INCQI model in quantum image preparation and visualization.
Main Methods:
- Development of the improved color digital image quantum representation (INCQI) model.
- Design of a quantum image control circuit based on the INCQI model.
- Experimental implementation and verification on the IBM Quantum Experience (IBMQ) platform.
Main Results:
- The INCQI model successfully processes color images and facilitates multi-channel transformations.
- Auxiliary quantum bits enable transparency information processing.
- Experimental results on IBMQ confirmed the feasibility and effectiveness of INCQI for quantum image preparation and visualization.
Conclusions:
- The proposed INCQI model offers a significant advancement in quantum image representation.
- It effectively addresses the limitations of previous models, enabling broader QIP applications.
- The successful experimental validation provides a strong foundation for future research in QIP.