Quantum Computing Approaches to Autonomous Mobile Robots and Multi-Machine Systems: A Perspective on Design Automation
| Authors | Nemala Jayasri1 , Pellakuri Vidyullatha1, , A. Saravanan2, Anil Kumar Muthevi3, K.P. Dinakaran4, Nageswara Rao Medikondu5 |
| Affiliations |
1Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, AP, India 2Department of Electronics and Communication Engineering, Jaya Engineering College Chennai 602024, Tamilnadu, India 3Department of Computer Science and Engineering, Aditya University Surampalem 533437, AP, India 4Department of EEE, Panimalar Engineering College, Bangalore Trunk Road, Varadharajapuram, Poonamallee, Chennai 600123, India 5Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, AP, India |
| Е-mail | pvidyullatha@kluniversity.in |
| Issue | Volume 17, Year 2025, Number 3 |
| Dates | Received 10 April 2025; revised manuscript received 23 June 2025; published online 27 June 2025 |
| Citation | Nemala Jayasri, Pellakuri Vidyullatha, [footnoteRef:], и др., J. Nano- Electron. Phys. 17 No 3, 03020 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(3).03020 |
| PACS Number(s) | 07.05.Tp, 07.07.Tw |
| Keywords | Quantum Annealing, D-Wave systems, Autonomous mobile robots, Task allocation, Optimization (14) , Smart factories. |
| Annotation |
Quantum Annealing (QA), particularly with D-Wave systems, presents a transformative solution for optimizing task allocation in autonomous mobile robots (AMRs) and multi-machine systems within Industry 6.0. Traditional scheduling methods often struggle to efficiently solve NP-hard optimization problems, which results in inefficient resource utilization, increased idle time, and production delays. Quantum Annealing overcomes these limitations by formulating task scheduling as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This allows quantum processors to explore multiple solution paths simultaneously, significantly speeding up the process of identifying near-optimal allocations. By leveraging the principle of quantum tunneling, QA is able to escape local minimum and find globally optimal or near-optimal solutions, ensuring balanced workload distribution among machines and minimizing production bottlenecks. In dynamic industrial environments, where real-time adjustments and adaptive scheduling are crucial, QA offers a significant advantage in continuously optimizing task assignments. This leads to enhanced manufacturing efficiency, reduced energy consumption, and more streamlined production workflows. As quantum hardware continues to evolve, the integration of QA-driven optimization with AI, IoT, and robotics will play a pivotal role in shaping the future of intelligent automation in smart factories, paving the way for higher productivity and cost-efficiency in manufacturing ecosystems. |
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List of References |
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