🤖 AI Summary
This study addresses the critical limitation of traditional supply chain risk models, which neglect interdependencies among nodes and thereby systematically underestimate cascading failure risks. The authors propose the first quantum-native framework that maps a four-tier, 40-node supply chain onto a 40-qubit Ising Hamiltonian. By integrating ADAPT-VQE with gradient-based operator screening and density-of-states quantum phase estimation, the framework enables simulation of correlated shock propagation, real-time ranking of intervention policies, and quantification of tail risks. A key innovation lies in leveraging quantum entanglement to model cascading failures, yielding a quantum risk measure compatible with Value-at-Risk (VaR). Demonstrated at industrial scale, the approach exhibits exponential computational advantages over classical Monte Carlo methods.
📝 Abstract
Classical supply chain risk models treat node failures as statistically independent events, systematically underestimating correlated cascade failures across multi-tier supplier networks. We present QR-SPPS (Quantum-Native Retail Shock Propagation and Policy Stress Simulator), a quantum-native framework for retail supply chain risk analysis implemented on the Qiskit ecosystem using OpenFermion-based Ising Hamiltonian encoding. A 40-node, four-tier supply network is mapped to a 40-qubit Hamiltonian with ZZ coupling terms representing correlated supplier dependencies. A hardware-efficient Variational Quantum Eigensolver (VQE) computes the stress ground state, revealing entangled cascade failures that differ substantially from classical Monte Carlo predictions. We further introduce the application of ADAPT-VQE gradient screening for counterfactual policy evaluation, enabling real-time ranking of six crisis interventions without repeated variational optimization. Finally, Density-of-States Quantum Phase Estimation (DOS-QPE) reconstructs the eigenspectrum through Trotter evolution and estimates Boltzmann-weighted catastrophe probabilities as a function of market-volatility temperature, providing a quantum-native tail-risk metric compatible with Value-at-Risk analysis. The framework demonstrates scalable quantum algorithms for correlated supply chain stress propagation, policy optimization, and systemic risk quantification while highlighting the exponential computational barriers faced by classical simulation at industrial-scale problem sizes.