Quantum Parallel Processing Framework for Image Processing Applications

• Published in: Proceedings - International Symposium on Autonomous Decentralized Systems pp. 16 - 20
• Main Authors: Salcedo, Juan Carlos, Ahmed, Khaled
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.07.2025
• Subjects: C++; Hadamard edge detection; Image edge detection; Instruction sets; open closed loop systems; Operating systems; Organizations; parallel distributed processing; Python; Quantum; Quantum algorithm; Qubit; Resource management; Scheduling; System integration; Testing
• ISSN: 2640-7485
• DOI: 10.1109/ISADS66912.2025.00007

Quantum computing stands at a transformative threshold, with error-correcting quantum processors expected to emerge within the next five years. This technological advancement will be crucial for maintaining competitive advantage in fields such as cryptography, optimization, molecular medicine, and image processing, with mainstream quantum computing projected by 2030. This study introduces a novel Quantum Parallel Framework (QPF) designed to prepare for the integration of quantum computing in industrial applications. The research focuses on developing and implementing parallel quantum algorithms for practical commercial applications, particularly in image processing. The QPF provides a comprehensive methodology for organizations to build expertise in quantum computing integration with classical systems. It includes a Qiskit interface for managing quantum algorithms and communications with Quantum Processing Units (QPUs), along with a C++ and OpenGL-based front end. The framework's capabilities are demonstrated through a case study implementing a Quantum Hadamard Edge Detector (QHED) in parallel execution. The study evaluates various performance parameters, including QHED threads, processors, Non-Uniform Memory Allocation (NUMA) strategy, thread priority, and image resolution, to optimize processing efficiency. The framework provides a practical pathway for organizations to develop and mature their quantum computing capabilities, focusing on real-world applications and system integration. This work contributes to the growing field of quantum computing by offering a methodology that can be adapted for various quantum algorithms and applications, particularly in preparation for more powerful self-error correcting QPUs.