Application prospects and challenges of quantum computing in complex mine ventilation and safety systems

As shallow coal resources become increasingly depleted, deep coal mining is key to ensuring national energy security. In deep mining, the spatiotemporal correlation characteristics of coal and gas outbursts and coal–rock–gas coupled dynamic disasters become more complex, with significantly enhanced chain effects and coupling behaviors, which makes mine ventilation and safety control more difficult. Based on an analysis of the advantages of quantum computing in information representation, information storage, and computational paradigms, as well as the challenges faced by complex mine ventilation and safety control in deep coal mining such as multiphysics coupling solutions and high-dimensional computation, this study explores the potential advantages of a full-chain technical system empowered by quantum computing for complex mine ventilation and safety control from microscopic mechanisms to macroscopic systems. These advantages include ① revealing microscopic disaster-inducing mechanisms of multiphysics coupling through quantum simulation, ② rapidly solving high-dimensional models through quantum parallel computation, ③ efficiently addressing combinatorial optimization problems in mine ventilation and safety systems through quantum combinatorial optimization algorithms while accelerating model training and deeply mining and integrating multi-source heterogeneous precursor information through quantum machine learning, ④ building an intelligent computing platform for mine ventilation and safety systems through the integration of artificial intelligence and quantum computing. The challenges of engineering applications of quantum computing are analyzed, including short quantum coherence time, susceptibility to environmental interference, limitations in the applicability of quantum algorithms, and constraints of quantum hardware facilities. Future research directions are identified as exploring feasibility through multi-instance verification, the superiority of engineering applications, and adaptability across multiple scenarios. This study aims to provide a theoretical reference for promoting the development of intelligent mine ventilation and safety systems toward the integration of artificial intelligence, quantum computing, and quantum artificial intelligence.

随着浅部煤炭资源日渐枯竭，深部煤炭开采成为保障国家能源安全的核心支撑。在深部开采场景中，煤与瓦斯突出、煤–岩–气耦合动力灾害的时空关联特征愈发复杂，链效应与耦合行为显著增强，使得矿井通风与安全管控难度大幅提升。本研究在分析量子计算（quantum computing）在信息表征、信息存储与计算范式层面的固有优势，以及深部煤炭开采中复杂矿井通风与安全管控所面临的多物理场耦合求解、高维计算等核心挑战的基础上，探讨了由量子计算赋能的全链条技术体系在复杂矿井通风与安全管控领域（从微观机制到宏观系统）的潜在应用价值。该技术体系的优势具体包括：① 通过量子模拟（quantum simulation）揭示多物理场耦合的微观致灾机制；② 依托量子并行计算（quantum parallel computation）快速求解高维模型；③ 借助量子组合优化算法（quantum combinatorial optimization algorithms）高效解决矿井通风与安全系统中的组合优化难题，同时加速模型训练，并通过量子机器学习（quantum machine learning）深度挖掘与融合多源异构前兆信息；④ 通过人工智能与量子计算的融合构建矿井通风与安全系统智能计算平台。本研究同时分析了量子计算工程化应用所面临的多重挑战，包括量子相干时间（quantum coherence time）较短、易受环境干扰、量子算法适用性存在局限，以及量子硬件设施（quantum hardware facilities）供给约束。未来研究方向可聚焦于通过多实例验证（multi-instance verification）探索技术可行性、验证工程应用优越性，以及拓展多场景适配性。本研究旨在为推动矿井通风与安全智能系统朝着人工智能、量子计算与量子人工智能（Quantum Artificial Intelligence）融合的方向发展提供理论参考。