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IEEE Internet of Things Journal

Exponentially Scalable Quantum Reinforcement Learning for Large-Scale Network Scheduling

Gyu Seon Kim, Emily Jimin Roh, Soohyun Park, Joongheon Kim

Published Jul 29, 2026• 0 citations
Publisher
DOI
10.1109/jiot.2026.3717839

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Modern cloud systems demand efficient task scheduling to optimize resource utilization and meet QoS requirements amid unpredictable workloads. Classical Reinforcement Learning (RL) struggles with slow convergence and poor scalability in these complex environments due to exponentially growing state-action spaces. Quantum Reinforcement Learning (QRL) addresses these limitations by exploiting superposition and entanglement to enable parallel policy exploration and faster learning. This survey analyzes recent QRL and quantum-inspired schedulers across cloud, edge, and fog computing, highlighting their superior performance over classical Deep Reinforcement Learning (DRL) baselines in simulated settings. Key findings reveal persistent methodological gaps, including an exclusive reliance on noise-free quantum simulators, an absence of real hardware validation, and limited scalability testing. Future research directions include prioritizing noise-resilient designs, developing hybrid quantum-classical systems for NISQ-era deployment and integrating data-aware scheduling algorithms.