用量子-经典混合模型提升供应链数字孪生的不确定性量化能力
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach
- 结合量子特征变换与经典机器学习，构建混合不确定性量化框架
- 量子比特数从4增至16时，模型对异常样本的响应差异显著提升
- 适用于需高韧性决策的动态供应链与金融风险场景
本研究探索在复杂动态领域（如供应链数字孪生韧性、金融风险评估）中，利用量子-经典混合机器学习模型进行不确定性量化（UQ）。尽管量子特征变换已用于复杂数据任务，但其在混合架构中对不确定性传播的影响尚不明确。本文在混合框架下应用多种现有UQ技术，分析量子特征变换对不确定性传播的作用。实验显示，当量子比特数从4增至16时，模型对异常检测（OD）样本的响应呈现明显差异，这对动态环境中的韧性决策至关重要。结果表明，量子计算可有效转换数据特征以增强不确定性量化，尤其与经典方法结合时效果更优。
原文摘要 · Abstract (English)
This study investigates uncertainty quantification (UQ) using quantum-classical hybrid machine learning (ML) models for applications in complex and dynamic fields, such as attaining resiliency in supply chain digital twins and financial risk assessment. Although quantum feature transformations have been integrated into ML models for complex data tasks, a gap exists in determining their impact on UQ within their hybrid architectures (quantum-classical approach). This work applies existing UQ techniques for different models within a hybrid framework, examining how quantum feature transformation affects uncertainty propagation. Increasing qubits from 4 to 16 shows varied model responsiveness to outlier detection (OD) samples, which is a critical factor for resilient decision-making in dynamic environments. This work shows how quantum computing techniques can transform data features for UQ, particularly when combined with classical methods.
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