Abstract

The integration of quantum computing into image processing represents a significant advancement in computational technology, offering transformative capabilities that enhance performance, efficiency, and accuracy. This chapter explores hybrid quantum-classical algorithms, focusing on how quantum subroutines can be integrated into classical image processing workflows. A comparative analysis of various quantum image encoding techniques highlights their strengths and limitations, paving the way for optimized applications across diverse fields. Key advantages of quantum-enhanced image processing, such as improved speed in high-resolution data analysis and superior optimization methods, are discussed in relation to real-world applications, including medical imaging and computer vision. The chapter anticipates future developments in quantum technology, emphasizing the need for interdisciplinary collaboration to unlock the full potential of quantum computing in digital image applications. By addressing the critical gaps in current methodologies, this work aims to inspire further research and innovation in the realm of quantum-enhanced image processing.

Introduction

The emergence of quantum computing marks a revolutionary shift in computational paradigms, offering unprecedented capabilities for processing complex data [1]. Among its various applications, quantum computing holds particular promise for enhancing image processing techniques [2,3]. Traditional image processing relies on classical computing methods, which, while effective, often struggle to manage the ever-increasing complexity and size of modern image datasets [4,5,6,7]. The integration of quantum algorithms into image processing workflows offers a new avenue for overcoming these challenges, providing the potential for faster processing times and improved accuracy in image analysis [8,9].

Quantum computing operates on fundamentally different principles compared to classical computing [10,11]. While classical computers use bits as the smallest unit of data, representing information in binary states (0 or 1), quantum computers utilize quantum bits or qubits [12]. Qubits can exist in multiple states simultaneously, thanks to the phenomena of superposition and entanglement [13]. This unique property enables quantum computers to perform parallel computations, dramatically accelerating the processing of large datasets [14]. Such advancements are particularly beneficial in image processing, where tasks such as filtering, segmentation, and recognition can be significantly expedited through the use of quantum algorithms [15,16,17].