Quantum-Inspired Reinforcement Learning Models for Ultra-Fast Optimization in Large-Scale Systems

Abstract

The exponential growth of large-scale, high-dimensional systems in fields such as logistics, telecommunications, energy networks, and cloud computing has created a pressing need for ultra-fast and scalable optimization models. Classical reinforcement learning (RL), although powerful, often struggles with slow convergence, high computational overhead, and limited performance when facing massive state–action spaces. Inspired by principles of quantum mechanics—superposition, entanglement, and probabilistic amplitude encoding—Quantum-Inspired Reinforcement Learning (QIRL) offers a transformative solution to accelerate decision-making and improve global optimality. This paper proposes a novel QIRL framework that integrates quantum-inspired state encoding, amplitude-based exploration, and quantum-behavior policy updates into traditional RL pipelines. By leveraging quantum-inspired operators for value propagation and policy selection, the proposed model achieves significant reductions in exploration time and enhances convergence efficiency. Experimental evaluations conducted on large-scale optimization tasks, including multi-agent routing, dynamic resource allocation, and energy-aware cloud scheduling, demonstrate that QIRL outperforms classical RL models in terms of solution quality, convergence speed, and computational throughput. The findings highlight the potential of quantum-inspired mechanisms to deliver near-quantum performance on classical hardware, thereby establishing QIRL as a highly promising pathway toward ultra-fast optimization in next-generation intelligent systems.