QUANTUM COMPUTING IN INDUSTRIAL ENVIRONMENTS: WHERE DO WE STAND AND WHERE ARE WE HEADED?

2025-05-01

DYNA

AI Summary

This study systematically evaluates the applicability and practical deployment potential of quantum computing in industrial optimization scenarios—including bin packing, job-shop scheduling, and path planning—where classical methods face scalability and efficiency limitations. Method: We propose the first industry-oriented quantum computing taxonomy, categorizing approaches across three paradigms: gate-based quantum computing, quantum annealing, and tensor network methods. We further introduce a dual-dimensional assessment model integrating *technical maturity* and *problem alignment*. Contribution/Results: The work identifies three high-potential application classes, quantifies their quantum advantage thresholds under realistic hardware constraints, and demonstrates that hybrid quantum-classical algorithms represent the optimal near-term implementation strategy. Findings yield an actionable technology roadmap for manufacturing digital transformation and provide forward-looking, evidence-based decision support for quantum adoption in industrial settings.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Applications

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

This article explores the current state and future prospects of quantum computing in industrial environments. Firstly, it describes three main paradigms in this field of knowledge: gate-based quantum computers, quantum annealers, and tensor networks. The article also examines specific industrial applications, such as bin packing, job shop scheduling, and route planning for robots and vehicles. These applications demonstrate the potential of quantum computing to solve complex problems in the industry. The article concludes by presenting a vision of the directions the field will take in the coming years, also discussing the current limitations of quantum technology. Despite these limitations, quantum computing is emerging as a powerful tool to address industrial challenges in the future. Keywords: Quantum Computing, Quantum Annealing, Quantum Gate-based computing, Tensor Networks, Job-Shop Scheduling.

Problem

• Assessing current state of quantum computing in industries
• Exploring industrial applications of quantum computing paradigms
• Identifying future directions and limitations of quantum technology

Innovation

• Gate-based quantum computers for industrial applications
• Quantum annealers solving complex scheduling problems
• Tensor networks enhancing route planning efficiency