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 traditional methods.

翻译：本研究探讨了在复杂动态领域（如实现供应链数字孪生韧性与金融风险评估）中，利用量子-经典混合机器学习模型进行不确定性量化的问题。尽管量子特征变换已集成至机器学习模型以处理复杂数据任务，但其在混合架构（量子-经典方法）中对不确定性量化的影响尚缺乏系统研究。本工作将现有不确定性量化技术应用于混合框架内的不同模型，探究量子特征变换如何影响不确定性的传播。实验表明，将量子比特数从4增至16时，模型对异常检测样本的响应呈现显著差异，这在动态环境中是实现韧性决策的关键因素。本研究揭示了量子计算技术如何通过与经典方法结合，为不确定性量化提供创新的特征转换途径。