Noor Ahmed

Inspired by the ideas of quantum physics, quantum computing is a modern computational paradigm. It is one of the rapidly growing areas in computer science, mathematics, and physics. It has high speed and efficiency by making use of phenomena such as superposition, entanglement, and interference, allowing new possibilities in areas like cryptography, machine learning, and complex system simulations. Even though it has great potential, quantum computing also suffers from resource management and task scheduling issues, system design due to the differences in the foundational basis between classical and quantum computing. This study reviews the architecture and workflow of QOS (quantum operating system), emphasizing the integration of quantum and classical resources. In this study, elements such as quantum resource management, automatic qubit calibration, quantum program compilation, and hybrid task scheduling are investigated. The present study also looks at innovative architectures such as quantum FPGAs, x86-based quantum systems, and distributed quantum systems, which provide advanced quantum computations while being compatible with classical systems. The findings of this paper reveal how QOS improves fidelity, resource utilization, and task scheduling. We have also explored key quantum algorithms such as Grover’s and Shor’s algorithms, highlighting their potential in NP-hard problems[1], quantum machine learning, and optimization challenges. This research addresses the transformative power of QOS in solving real problems in computation, providing robust solutions in the management and programming of quantum resources. This research advances the development of future quantum computing systems by evaluating existing designs and suggesting improvements in task scheduling, qubit accuracy, and quantum algorithm performance. The findings in this paper seek to contribute to the advancement of understanding of QOS and inspire innovation in several fields.