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<p>The complexity of real-time tactical decision making in volleyball presents significant computational challenges due to high-dimensional state spaces, multi-agent interactions, and stringent temporal constraints. This research presents a novel quantum-enhanced hybrid deep reinforcement learning framework that integrates quantum computing principles with classical neural networks to optimize tactical decision making in competitive volleyball scenarios. The proposed framework incorporates quantum variational circuits for neural network parameter optimization, quantum state encoding mechanisms for efficient high-dimensional feature representation, and quantum parallel processing algorithms to accelerate training convergence. Experimental evaluation using quantum circuit simulations on the Qiskit framework demonstrates substantial performance improvements compared to traditional deep reinforcement learning approaches, achieving 95.4% decision accuracy versus 82.1% for classical methods, 2.8-fold acceleration in convergence speed (387 epochs versus 1456 epochs), and real-time response latencies of 23.7 milliseconds well within the 50-millisecond threshold required for competitive volleyball. It should be noted that all reported performance metrics are obtained from quantum simulator experiments rather than execution on actual quantum hardware, and practical deployment on near-term quantum devices may yield different results due to hardware noise and decoherence effects. The tactical effectiveness assessment reveals 89.3% success rates in realistic volleyball scenarios while maintaining robust performance across varying opponent strategies and environmental conditions. The quantum enhancement mechanisms leverage superposition and entanglement properties to capture complex multi-player tactical dependencies more efficiently than classical approaches. This research establishes quantum machine learning as a transformative technology for sports intelligence analysis, providing foundational evidence for quantum-enhanced decision making applications across diverse competitive athletics domains.</p>

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Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月20日

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  • Nan Cai,
  • Minghui Zhao,
  • Yantong Ke,
  • Xin Liu

摘要

The complexity of real-time tactical decision making in volleyball presents significant computational challenges due to high-dimensional state spaces, multi-agent interactions, and stringent temporal constraints. This research presents a novel quantum-enhanced hybrid deep reinforcement learning framework that integrates quantum computing principles with classical neural networks to optimize tactical decision making in competitive volleyball scenarios. The proposed framework incorporates quantum variational circuits for neural network parameter optimization, quantum state encoding mechanisms for efficient high-dimensional feature representation, and quantum parallel processing algorithms to accelerate training convergence. Experimental evaluation using quantum circuit simulations on the Qiskit framework demonstrates substantial performance improvements compared to traditional deep reinforcement learning approaches, achieving 95.4% decision accuracy versus 82.1% for classical methods, 2.8-fold acceleration in convergence speed (387 epochs versus 1456 epochs), and real-time response latencies of 23.7 milliseconds well within the 50-millisecond threshold required for competitive volleyball. It should be noted that all reported performance metrics are obtained from quantum simulator experiments rather than execution on actual quantum hardware, and practical deployment on near-term quantum devices may yield different results due to hardware noise and decoherence effects. The tactical effectiveness assessment reveals 89.3% success rates in realistic volleyball scenarios while maintaining robust performance across varying opponent strategies and environmental conditions. The quantum enhancement mechanisms leverage superposition and entanglement properties to capture complex multi-player tactical dependencies more efficiently than classical approaches. This research establishes quantum machine learning as a transformative technology for sports intelligence analysis, providing foundational evidence for quantum-enhanced decision making applications across diverse competitive athletics domains.