Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach
It addresses uncertainty quantification for supply chain and financial risk applications, but is incremental as it applies existing techniques in a hybrid framework.
This study tackled uncertainty quantification in supply chain digital twins by applying quantum-classical hybrid machine learning models, finding that increasing qubits from 4 to 16 varied model responsiveness to outlier detection samples.
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.