Do AI Markets Drive Financial Performance in Chinese Banks? A Quantum-Inspired (QI) MCDM Approach
提出量子启发式多准则决策框架，评估中国银行在人工智能技术背景下的结构绩效，发现银行类型间效率差异显著，且较大的人工智能和智慧城市市场与系统性纠缠减少相关。
Abstract This paper proposes a novel quantum-inspired multi-criteria decision-making (QI-MCDM) framework to assess the structural performance of Chinese banks considering emerging AI technological contexts. By embedding classical bank performance indicators within a quantum probability space, the model captures inter-criteria entanglement, decoherence from ideal benchmarks, and robustness under noise—constructs traditionally absent in conventional MCDM models. Empirical results reveal significant divergence in structural efficiency across bank types. Top-performing banks exhibit higher adaptability, often tied to agile governance and fintech integration, whereas lower-performing institutions are encumbered by legacy systems and structural fragmentation. Regression and random forest analyses further show that larger AI and smart city markets are paradoxically associated with reduced systemic entanglement, suggesting that contextual technological maturity fosters functional decoupling among traditional banking metrics. These findings provide theoretical and managerial insights into how technological complexity reshapes financial performance structures in emerging economies.