QR-SPPS：通过变分量子本征求解器、自适应变分量子本征求解器反事实策略排序和态密度量子相位估计玻尔兹曼尾部风险量化实现量子原生零售供应链风险模拟
QR-SPPS: Quantum-Native Retail Shock Propagation and Policy Stress Simulator
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中文总结 AI 辅助
研究零售供应链风险，提出QR-SPPS框架，用基于OpenFermion的伊辛哈密顿量编码，通过VQE、ADAPT-VQE、DOS-QPE实现应力传播、政策评估及风险量化，展示可扩展量子算法，凸显经典模拟局限。
中文摘要 AI 辅助
经典供应链风险模型将节点故障视为统计独立事件，系统性低估了多层供应商网络中的相关级联故障。我们提出了QR-SPPS（量子原生零售冲击传播与政策压力模拟器），这是一个在Qiskit生态系统上使用基于OpenFermion的伊辛哈密顿量编码实现的零售供应链风险分析量子原生框架。一个40节点、四层的供应网络被映射到一个带有ZZ耦合项的40量子比特哈密顿量，其代表相关的供应商依赖关系。硬件高效的变分量子本征求解器（VQE）计算应力基态，揭示出与经典蒙特卡洛预测有显著差异的纠缠级联故障。我们还引入了自适应变分量子本征求解器梯度筛选用于反事实政策评估，能够对六种危机干预措施进行实时排序而无需重复变分优化。最后，态密度量子相位估计（DOS-QPE）通过Trotter演化重建本征谱，并估计作为市场波动温度函数的玻尔兹曼加权灾难概率，提供了一个与风险价值分析兼容的量子原生尾部风险度量。该框架展示了用于相关供应链应力传播、政策优化和系统风险量化的可扩展量子算法，同时突出了经典模拟在工业规模问题大小上面临的指数级计算障碍。
英文摘要
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.
