Workforce Task Execution Scheduling Using Gate-Based Quantum Computers

Recent advancements in quantum computing technology have heralded a new era of computational capabilities, outpacing classical simulations in solving complex problems. This remarkable progress has sparked a burgeoning interest in quantum algorithms across various sectors, with optimization emerging as a particularly vibrant field of study. A focal point of this field is the development and refinement of optimization algorithms that are suitable for current and emerging capability of quantum computers. These advancements are crucial as they offer practical ways to apply quantum computing to real-world challenges. Among these applications, personnel and task execution scheduling is a compelling use case for quantum computing as applied to solve complex optimization problems critical to business and industrial operations. In this paper, we explore the utilization of quantum computers to address this class of optimization problems. Employing a range of advanced techniques, including a novel method that improves efficiency, our method demonstrates the capability of quantum computing to provide effective solutions for intricate optimization problems. We particularly focus on the application of IBM's 127 -qubit quantum devices to manage scheduling tasks in workforce management, involving 500 to 874 binary decision variables and over 1000 constraints. This exploration serves not only as a proof of concept for the practical applications of quantum computing but also as a testament to its evolving role in solving business-critical optimization challenges.