Classical supply chain risk models treat node failures as statistically independent events, systematically underestimating cascade probabilities when supplier dependencies are strongly correlated. At n=40 nodes, the full correlated failure distribution requires O(2^n) classical samples, a regime where exact simulation demands 17.6 TB of memory and over 369,000 hours of computation on a standard workstation. We present QR-SPPS (Quantum-Native Retail Shock Propagation and Policy Stress Simulator), a three-algorithm quantum pipeline implemented using the Qiskit framework with the Aer statevector_simulator backend. First, a 40-node, 4-tier retail supply network is encoded as a 40-qubit Ising Hamiltonian using OpenFermion QubitOperator, where ZZ coupling terms encode correlated cascade probabilities structurally absent from classical Monte Carlo. Second, a hardware-efficient VQE circuit finds the ground-state stress distribution with zero error, detecting entangled cascade failures in 14/40 nodes with max|ΔP|=0.637 versus classical Monte Carlo. Third, we introduce the first application of ADAPT-VQE gradient screening to counterfactual macroeconomic policy evaluation: six crisis interventions are ranked in O(1) Qiskit operator evaluations per policy, a 287x speedup over sequential VQE re-optimisation. Fourth, Density-of-States QPE (DOS-QPE) reconstructs the full eigenspectrum via 32-step Trotter evolution and introduces a novel mapping of the Boltzmann catastrophe probability P_cat(T) to VIX-equivalent market volatility temperature, enabling direct integration into regulatory Value-at-Risk frameworks. Qiskit Aer scaling benchmarks confirm exponential classical intractability at 40 qubits.
Comments
7 pages, 6 figures, 8 tables, 2 algorithms. Implemented in Qiskit with Aer statevector_simulator and OpenFermion. Cross-list to cs.ET and quant-ph requested
Classical supply chain risk models treat node failures as statistically independent events, systematically underestimating cascade probabilities when supplier dependencies are strongly correlated. At n=40 nodes, the full correlated failure distribution requires O(2^n) classical samples, a regime where exact simulation demands 17.6 TB of memory and over 369,000 hours of computation on a standard workstation. We present QR-SPPS (Quantum-Native Retail Shock Propagation and Policy Stress Simulator), a three-algorithm quantum pipeline implemented using the Qiskit framework with the Aer statevector_simulator backend. First, a 40-node, 4-tier retail supply network is encoded as a 40-qubit Ising Hamiltonian using OpenFermion QubitOperator, where ZZ coupling terms encode correlated cascade probabilities structurally absent from classical Monte Carlo. Second, a hardware-efficient VQE circuit finds the ground-state stress distribution with zero error, detecting entangled cascade failures in 14/40 nodes with max|ΔP|=0.637 versus classical Monte Carlo. Third, we introduce the first application of ADAPT-VQE gradient screening to counterfactual macroeconomic policy evaluation: six crisis interventions are ranked in O(1) Qiskit operator evaluations per policy, a 287x speedup over sequential VQE re-optimisation. Fourth, Density-of-States QPE (DOS-QPE) reconstructs the full eigenspectrum via 32-step Trotter evolution and introduces a novel mapping of the Boltzmann catastrophe probability P_cat(T) to VIX-equivalent market volatility temperature, enabling direct integration into regulatory Value-at-Risk frameworks. Qiskit Aer scaling benchmarks confirm exponential classical intractability at 40 qubits.
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.
Classical Monte Carlo methods for pricing catastrophe insurance tail risk converge at order reciprocal root N, requiring large simulation budgets to resolve upper-tail percentiles of the loss distribution. This sample-sparsity problem can lead to AI models trained on impoverished tail data, producing poorly calibrated risk estimates where insolvency risk is greatest. Quantum Amplitude Estimation (QAE), following Montanaro, achieves convergence approaching order reciprocal N in oracle queries - a quadratic speedup that, at scale, would enable high-resolution tail estimation within practical budgets. We validate this advantage empirically using a Qiskit Aer simulator with genuine Grover amplification. A complete pipeline encodes fitted lognormal catastrophe distributions into quantum oracles via amplitude encoding, producing small readout probabilities that enable safe Grover amplification with up to k=16 iterations. Seven experiments on synthetic and real (NOAA Storm Events, 58,028 records) data yield three main findings: an oracle-model advantage, that strong classical baselines win when analytical access is available, and that discretisation, not estimation, is the current bottleneck.
This article presents a quantum-enhanced framework for optimizing complex supply chain networks (SCNs) under stochastic demand, addressing computational intractability in classical approaches. We propose a hybrid quantum-classical methodology integrating the Quantum Approximate Optimization Algorithm (QAOA) with hierarchical graph decomposition, enabling industrial-scale optimization (?100 nodes) on NISQ-era hardware. Key innovations include: (1) First quantum-native stochastic demand modeling via amplitude estimation, achieving quadratic speedup (O(log(1/?)/?)) over Monte Carlo methods; (2) A novel decomposition algorithm coordinating quantum subproblems through augmented Lagrangian optimization; (3) Error-adapted QAOA with zero-noise extrapolation, improving quantum volume utilization to 85%. Empirical validation demonstrates 42% faster computation versus classical solvers (CPLEX) while maintaining solution quality within 4.7% of optimality. The framework exhibits near-linear scalability (efficiency = 0.92) and outperforms heuristics in high-connectivity, volatile-demand scenarios. Mathematical contributions include a stochastic Hamiltonian formulation for normally distributed demand and noise-aware parameter optimization. This work establishes quantum computing as a viable tool for real-time SCN optimization, particularly for multi-period networks with uncertainty. Future research will explore multi-objective extensions, quantum machine learning integration, and dynamic reconfiguration protocols.
Traffic jams in a single port destabilize global supply systems, and a week of unexpected demand can derail carefully planned timetables. Even with machine learning, conventional optimization discards uncertainty and folds risk into static buffers or heuristic safety stocks. That method fails when interruptions cascade and supplier, route, and market signal linkages change hourly. We used quantum computation to reimagine that loop, where classical tools are awkward. Quantum Contextual Supply Chain Optimization under Uncertainty integrates five interconnecting methods rather than bolting them on as modules. First, Contextual Quantum Demand Graph Embedding (CQDGE) encodes supplier-customer dependencies as quantum states, providing a fuller picture than time-series predictors. Quantum-Aware Contextual Policy Gradient (QCPG) uses variational quantum circuits to direct routing and allocation in messy, nonconvex landscapes, reducing convergence time. CQRAS samples risk-weighted schedules through quantum annealing and feeds its output into a Quantum-Integrated Digital Twin Optimizer (QIDTO) that stress-tests plans against detailed virtual replicas of trucks, warehouses, and fuel markets to avoid surprises like port shutdowns. Finally, Quantum-Backpropagated Global Reinforcement Integrator (Q-BGRI) updates embeddings and policies in one differentiable loop to fold the entire experience upstream. Early studies show double-digit delivery time savings and energy cost reductions, but practical deployments may uncover unmodeled oddities. Contextual embeddings and quantum reinforcement may offer a way beyond fragile, one-shot optimizers that dominate present methods.
Abstract Renewable energy supply chains — dependent on geographically concentrated raw materials such as lithium, cobalt, rare earths, and polysilicon — are increasingly exposed to procurement disruption risk from geopolitical shocks, commodity price volatility, and demand shifts tied to climate policy. This study develops and empirically tests a hybrid quantum-classical anomaly detection framework using a Variational Quantum Circuit (VQC) to flag early-warning disruption signals in renewable energy procurement data, and benchmarks it against three classical baselines: Isolation Forest, One-Class SVM, and an LSTM Autoencoder. A monthly panel (2008–2025, n = 216) combining the Caldara–Iacoviello Geopolitical Risk Index, World Bank Pink Sheet commodity prices, and IRENA renewable capacity data was engineered into 147 rolling-window features and reduced via PCA to six components for a 6-qubit angle-encoded circuit, implemented independently in PennyLane and Qiskit. Using three documented macro-disruption windows (2008–09 Global Financial Crisis, COVID-19 onset, and the 2022 Russia-Ukraine invasion) as ground truth, the VQC was evaluated against the classical baselines on precision, recall, F1-score, false-positive rate, per-typology recall, noise robustness, and computational cost. Contrary to the study's initial hypotheses, Isolation Forest achieved the highest F1-score (0.593) among all five models, modestly outperforming both VQC implementations (F1 = 0.556). The VQC showed no measurable robustness advantage over Isolation Forest at any tested noise level (0–50% of feature standard deviation) and required approximately 1,230 times longer to train than Isolation Forest for a comparable or weaker result. All models, including the VQC, failed to detect the COVID-19-era logistics disruption window. These findings suggest that, on this dataset and circuit design, near-term quantum machine learning does not yet offer a measurable advantage over well-tuned classical anomaly detectors for renewable energy procurement risk monitoring, and the paper discusses the practical and methodological implications of this null result for procurement risk management and future QML research.
Traffic jams in a single port destabilize global supply systems, and a week of unexpected demand can derail carefully planned timetables. Even with machine learning, conventional optimization discards uncertainty and folds risk into static buffers or heuristic safety stocks. That method fails when interruptions cascade and supplier, route, and market signal linkages change hourly. We used quantum computation to reimagine that loop, where classical tools are awkward. Quantum Contextual Supply Chain Optimization under Uncertainty integrates five interconnecting methods rather than bolting them on as modules. First, Contextual Quantum Demand Graph Embedding (CQDGE) encodes supplier-customer dependencies as quantum states, providing a fuller picture than time-series predictors. Quantum-Aware Contextual Policy Gradient (QCPG) uses variational quantum circuits to direct routing and allocation in messy, nonconvex landscapes, reducing convergence time. CQRAS samples risk-weighted schedules through quantum annealing and feeds its output into a Quantum-Integrated Digital Twin Optimizer (QIDTO) that stress-tests plans against detailed virtual replicas of trucks, warehouses, and fuel markets to avoid surprises like port shutdowns. Finally, Quantum-Backpropagated Global Reinforcement Integrator (Q-BGRI) updates embeddings and policies in one differentiable loop to fold the entire experience upstream. Early studies show double-digit delivery time savings and energy cost reductions, but practical deployments may uncover unmodeled oddities. Contextual embeddings and quantum reinforcement may offer a way beyond fragile, one-shot optimizers that dominate present methods.