Problem
Research questions and friction points this paper is trying to address.
Quantum Optimisation
Job-Shop Scheduling Problem
Industrial Application
Quantum Annealing
Combinatorial Optimisation
A Reality Check on Quantum Optimisation: Evidence from an Industrial Case Study
🤖 AI Summary
This study addresses industrial job shop scheduling by developing a customized model that incorporates hardware constraints and systematically evaluates the performance of quantum annealing (D-Wave), digital annealing (Fujitsu), quantum-inspired algorithms, and classical approaches—including mixed-integer linear programming (MILP) and exact solvers—on platforms such as IBM Quantum. Through a hardware-software co-design methodology, the work demonstrates the practical utility of quantum and quantum-inspired techniques in enhancing the quality of approximate solutions, guiding solver selection, and integrating into classical computational workflows. The research establishes a scalable hybrid solving paradigm for industrial scheduling and validates its feasibility and potential during early-stage proof-of-concept demonstrations.
📝 Abstract
Quantum Processing Units promise speed-ups for selected computational problems, including combinatorial optimisation, but their industrial utility remains an open challenge. We study an industrial variant of the Job-Shop Scheduling Problem using quantum, quantum-inspired, and classical methods across three platforms: IBM Quantum, the D-Wave Quantum Annealer, and the Fujitsu Digital Annealer. By tailoring formulations to hardware-specific constraints, we show that hardware-software co-design is essential for solution quality and scalability. We benchmark all approaches against an exact classical solver and a MILP formulation, evaluating runtime, solution quality, and scalability. Our results indicate that quantum and quantum-inspired optimisation can support industrial solver selection, integration in classical workflows, modelling decisions, and early proof-of-concept development, while suggesting a potential path towards improved approximations for industrial scheduling.
Innovation
Methods, ideas, or system contributions that make the work stand out.
hardware-software co-design
quantum optimisation
Job-Shop Scheduling Problem
quantum annealing
industrial benchmarking
🔎 Similar Papers
No similar papers found.
