Enhancing Performance in Resource-Constrained Environments through Quantum-Inspired Innovations in Mobile Cloud Systems
Keywords:
Edge computing, Energy latency trade-off, Mobile cloud computing, Quantum-inspired optimization, Task offloadingAbstract
The latency-sensitive, compute-intensive applications are increasingly demanding mobile cloud systems like augmented reality, IoT analytics, and real-time data processing. Mobile devices, however, are apt to run on extraordinarily resource-constrained designs with unyieldingly small shares of compute power, intermittent network bandwidth, and unchangeably restricted power. Under these conditions, the conventional methods used to perform task offloading, routing, and scheduling do not have a trade-off on energy efficiency and latency. This paper suggests a quantum-inspired hybrid architecture of mobile-edge-cloud systems that utilizes quantum-inspired optimization algorithms, such as Quantum-Inspired Evolutionary Algorithms (QIEA) and Quantum-Inspired Ant Colony Optimization (QACO), among other heuristics. Our scheme cuts on average the task latency by up to 35% in the simulation work, cuts the energy consumed by mobile devices by about 28% and the use of resources by about 22 compared to classical baselines. We present the design, algorithm techniques, and systems architecture, and we give an in-depth experimental procedure of the latency, energy, and robustness measurements. Perhaps, the latest developments in quantum-inspired computing can be used to boost the functionality of mobile-cloud systems functioning in a limited environment considerably with the assistance of QI techniques. We conclude with trade-offs, limitations, and future research, such as deploying hybrid quantum-classical implementation, security, and actual experimentations in next-generation mobile-edge-cloud systems.
References
Andreou, C. X. Mavromoustakis, E. K. Markakis, A. Bourdena, and G. Mastorakis, “Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing,” IEEE Access, vol. 13, pp. 54622–54635, 2025, doi: https://doi.org/10.1109/access.2025.3554024
M. Al Moteri, S. Bhatia, and M. Alojail, “Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT,” Mdpi.com, vol. 11, no. 6, pp. 308–308, Jun. 2023, doi: https://doi.org/10.3390/systems11060308
T. L. Duc, R. G. Leiva, P. Casari, and P.-O. Östberg, “Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing,” ACM Computing Surveys, vol. 52, no. 5, pp. 1–39, Oct. 2019, doi: https://doi.org/10.1145/3341145
S. Wang, M. Chen, X. Liu, C. Yin, and S. Cui, “A Machine Learning Approach for Task and Resource Allocation in Mobile-Edge Computing-Based Networks,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1358–1372, Feb. 2021, doi: https://doi.org/10.1109/jiot.2020.3011286
J. Wang, L. Zhao, J. Liu, and N. Kato, “Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach,” IEEE Transactions on Emerging Topics in Computing, pp. 1–1, 2019, doi: https://doi.org/10.1109/tetc.2019.2902661
Z. Ye, Y. Gao, Y. Xiao, M. Xu, H. Yu, and D. Niyato, “Cost-Effective Task Offloading Scheduling for Hybrid Mobile Edge-Quantum Computing,” arXiv.org, 2023. https://arxiv.org/abs/2306.14588
J. Khudair Madhloom, O. A. Hassen, and S. Mohamed Darwish, “A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks,” Electronics, vol. 12, no. 5, pp. 1171–1171, Feb. 2023, doi: https://doi.org/10.3390/electronics12051171
R. Juárez-Ramírez et al., “A Taxonomic View of the Fundamental Concepts of Quantum Computing–A Software Engineering Perspective,” Programming and Computer Software, vol. 49, no. 8, pp. 682–704, Dec. 2023, doi: https://doi.org/10.1134/s0361768823080108
M. Bey, P. Kuila, B. B. Naik, and S. Ghosh, “Quantum-inspired particle swarm optimization for efficient IoT service placement in edge computing systems,” Expert Systems with Applications, vol. 236, p. 121270, Feb. 2024, doi: https://doi.org/10.1016/j.eswa.2023.121270
U. Kumar Lilhore, R. Puthan Purayil, S. Simaiya, E. seif Ghith, H. G. Mohamed, and M. Khan, “QHRMOF: A Quantum-Inspired hybrid Multi-Objective framework for Energy-Efficient task scheduling and load balancing in cloud computing,” Journal of Cloud Computing Advances Systems and Applications, vol. 14, no. 1, Oct. 2025, doi: https://doi.org/10.1186/s13677-025-00777-2
M. Xu, D. Niyato, J. Kang, “Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing,” arXiv.org, 2025. https://arxiv.org/abs/2504.08134
M. Kumari, M. Sarkar, and N. R. Kumar, “Quantum-Inspired Artificial Bee Colony for Latency-Aware Task Offloading in IoV,” arXiv.org, 2025. https://arxiv.org/abs/2508.13637
U. Khalid, U. I. Paracha, and H. Shin, “Quantum Computing for Intelligent Transportation Systems: VQE-Based Traffic Routing and EV Charging Scheduling,” Mathematics, vol. 13, no. 17, pp. 2761–2761, Aug. 2025, doi: https://doi.org/10.3390/math13172761
