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.
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.
In recent years, quantum computing has emerged as a rapidly advancing interdisciplinary field, attracting growing attention from both academia and industry owing to its fundamentally novel approach to information processing and its potential to achieve computational capabilities beyond the reach of classical systems. This paper presents a structured review of quantum computing approaches applied to scheduling problems, which are among the most challenging NP-hard optimization problems. We first introduce fundamental principles of quantum computing, including gate-based models, quantum annealing, and hybrid frameworks. We then explore how various scheduling problems such as job-shop, flow-shop, and project scheduling-are encoded into quantum models, particularly using QUBO formulations suitable for current quantum devices. Finally, we discuss key limitations such as hardware constraints and model scalability, and we identify research directions to advance quantumenhanced scheduling methods. By grounding theory in empirical case studies and highlighting near-term deployable solutions, this study bridges the gap between quantum algorithm development and practical scheduling demands, offering a roadmap for researchers and practitioners in quantum optimization.
The advent of quantum computing promises transformative advancements across various scientific and technological domains. However, realizing this potential hinges critically on the efficient execution of quantum algorithms. A significant hurdle lies in the intricate orchestration of quantum resources – qubits, coherence times, and gate fidelity – to meet the demanding requirements of complex quantum algorithms. Traditional scheduling methods often fail to adequately account for these constraints, leading to suboptimal performance and increased execution times. This paper proposes a novel approach to quantum algorithm scheduling by formulating the problem as a Constraint Programming (CP) model. This approach leverages the power of CP solvers to systematically explore the solution space, identifying schedules that maximize resource utilization while adhering to stringent hardware limitations. We introduce a framework for representing quantum algorithms and their associated constraints within a CP environment, enabling automated optimization. The core of the method involves defining constraints related to qubit connectivity, coherence time limitations, and gate fidelity errors. The resulting CP model is then solved using state-of-the-art CP solvers, producing optimized schedules that demonstrate significant improvements in resource efficiency compared to naive scheduling strategies. This work represents a substantial advance in quantum algorithm scheduling, offering a robust and scalable solution for harnessing the full power of quantum hardware.
Optimization problems are pervasive across various domains, including logistics, finance, machine learning, and operations research. Quantum computing has emerged as a promising frontier to address these challenges, offering potential speedups for certain classes of optimization tasks. This paper explores the development and application of quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Grover’s search algorithm, tailored for optimization. Recent advancements in hardware, hybrid quantum-classical approaches, and variational techniques have enabled practical implementations on Noisy Intermediate-Scale Quantum (NISQ) devices. Challenges such as noise, scalability, and performance limitations are also discussed. Through theoretical analysis and case studies, this work demonstrates how quantum computing can complement classical methods, paving the way for breakthroughs in solving complex optimization problems.
Scheduling medical staff in healthcare institutions is a complex and critical task that requires balancing operational constraints, personnel availability, quality of care, and staff well-being. These interdependent factors make the creation of optimal schedules particularly challenging necessitating sophisticated approaches that effectively reconcile institutional needs and medical staff expectations. This project explores an innovative alternative approach: quantum optimization. By formulating the scheduling problem as a QUBO (Quadratic Unconstrained Binary Optimization) model, we aim to leverage the capabilities of quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA) This framework allows constraints and objectives to be reformulated as quadratic functions, facilitating their integration into Ising models and their resolution on quantum gate_based circuit systems.To this end, we implement a hybrid quantum-classical solution using QAOA on IBM’s Qiskit platform. We analyze QAOA’s performance on problem instances of up to 27 qubits, and compare it against classical solvers such as CPLEX. Through this comparative approach, we assess the practical viability of quantum optimization in medical staff scheduling and contribute to the broader investigation of quantum computing in operational planning within healthcare systems.
Quantum computing presents a transformative opportunity for optimizing supply chain and logistics operations by addressing complex combinatorial challenges beyond the capabilities of classical computing. Traditional supply chain optimization relies on heuristic and mathematical models, which struggle with large-scale problems such as vehicle routing, warehouse management, demand forecasting, and last-mile delivery. Quantum computing, leveraging qubits and principles like superposition and entanglement, enables exponential computational power and efficiency. This paper explores quantum computing's applications in logistics, including quantum annealing for route optimization, fleet management, and supply chain network design. Companies like IBM, Google, and D-Wave are advancing quantum algorithms, with hybrid quantum-classical approaches emerging as practical solutions. While large-scale commercial adoption is still in its infancy, research demonstrates promising results in reducing operational costs, improving sustainability, and enhancing decision-making. The study synthesizes over 80 published works on quantum computing's role in logistics, providing insights into emerging technologies, challenges, and future potential. As the field progresses, early adoption and pilot programs will be crucial for businesses to gain a competitive edge in an increasingly complex global supply chain landscape. Quantum computing has the potential to redefine efficiency, cost reduction, and real-time adaptability in logistics and supply chain management.
Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.