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Toward intelligent IoT scheduling with quantum-inspired latent models for energy and latency optimization
Tahir Alyas1, Qaiser Abbas2, Adeela Hayat3
1Department of Computer Science, Lahore Garrison University, Lahore, Pakistan. tahiralyas@lgu.edu.pk.
Scientific Reports
|January 19, 2026
Summary
Classical Internet of Things (IoT) scheduling struggles with latency and energy. This quantum-inspired framework improves IoT scheduling by learning latent representations and using global optimization, reducing latency and energy consumption.
Area of Science:
- Computer Science
- Quantum Computing
- Artificial Intelligence
Background:
- Classical Internet of Things (IoT) scheduling methods face challenges in optimizing latency and energy consumption.
- Existing methods like Genetic Algorithms and Reinforcement Learning exhibit limitations such as local search and slow convergence in dynamic IoT environments.
- These limitations lead to inefficient performance under varying workloads, round-trip time (RTT) fluctuations, and large-scale deployments.
Purpose of the Study:
- To introduce a novel quantum-inspired latent scheduling framework for IoT systems.
- To address the limitations of classical scheduling methods in achieving a balance between latency and energy usage.
- To enhance the adaptability and efficiency of IoT scheduling in heterogeneous and dynamic network conditions.
Main Methods:
- The proposed framework combines learning a latent representation using an encoder with quantum-inspired global optimization.
- A latent encoder is employed to uncover hidden task-resource interactions through small-task resource embeddings.
- Quantum-inspired search leverages superposition for exploration, enabling adaptation to changing network states and circumventing local minima.
Main Results:
- The quantum-inspired latent scheduling framework demonstrated significant improvements over classical baselines.
- Latency was reduced by up to 25%, and energy consumption was reduced by up to 28%.
- Convergence analysis and scalability tests confirmed the framework's superiority in multi-objective optimization for IoT scheduling.
Conclusions:
- The quantum-inspired latent scheduling framework offers a scalable, interpretable, and energy-efficient solution for next-generation IoT scheduling.
- The approach effectively handles complex task-resource interactions and dynamic network states.
- This work paves the way for advanced quantum-inspired latent modeling in IoT systems.
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