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arXiv:2601.20688

Grover's Search-Inspired Quantum Reinforcement Learning for Massive MIMO User Scheduling

Ruining Fan, Xingyu Huang, Mouli Chakraborty, Avishek Nag, Anshu Mukherjee

Published Jan 28, 2026• 0 citations
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Abstract

The efficient user scheduling policy in the massive Multiple Input Multiple Output (mMIMO) system remains a significant challenge in the field of 5G and Beyond 5G (B5G) due to its high computational complexity, scalability, and Channel State Information (CSI) overhead. This paper proposes a novel Grover's search-inspired Quantum Reinforcement Learning (QRL) framework for mMIMO user scheduling. The QRL agent can explore the exponentially large scheduling space effectively by applying Grover's search to the reinforcement learning process. The model is implemented using our designed quantum-gate-based circuit, which imitates the layered architecture of reinforcement learning, where quantum operations act as policy updates and decision-making units. Moreover, the simulation results demonstrate that the proposed method achieves proper convergence and significantly outperforms classical Convolutional Neural Networks (CNN) and Quantum Deep Learning (QDL) benchmarks.

DOI
10.48550/arxiv.2601.20688

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The efficient user scheduling policy in the massive Multiple Input Multiple Output (mMIMO) system remains a significant challenge in the field of 5G and Beyond 5G (B5G) due to its high computational complexity, scalability, and Channel State Information (CSI) overhead. This paper proposes a novel Grover's search-inspired Quantum Reinforcement Learning (QRL) framework for mMIMO user scheduling. The QRL agent can explore the exponentially large scheduling space effectively by applying Grover's search to the reinforcement learning process. The model is implemented using our designed quantum-gate-based circuit, which imitates the layered architecture of reinforcement learning, where quantum operations act as policy updates and decision-making units. Moreover, the simulation results demonstrate that the proposed method achieves proper convergence and significantly outperforms classical Convolutional Neural Networks (CNN) and Quantum Deep Learning (QDL) benchmarks.
We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNN) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling.
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.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35%\mathbf{2 0}-\mathbf{3 5 \%}20−35% faster convergence and 15%\mathbf{1 5 \%}15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.
Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.
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Reinforcement learning (RL) policy gradient methods, while effective, frequently encounter challenges related to instability and the sensitivity of their performance to the learning rate hyperparameter. This work introduces a novel approach leveraging quantum-inspired optimization techniques to address these issues. Specifically, we propose a quantum-inspired learning rate scheduler designed to dynamically adapt the update step size during policy gradient training. This scheduler, rooted in principles of quantum measurement and superposition, aims to provide a more robust and efficient learning process. The core claim of this paper is that the integration of this quantum-inspired learning rate scheduler significantly improves the stability and convergence speed of policy gradient methods. We present a theoretical framework and explore the potential of this approach, demonstrating its promise for enhancing RL performance. The proposed method offers a new direction in RL optimization, potentially bridging the gap between classical and quantum-inspired approaches.