QR-SPPS: Quantum-Native Retail Shock Propagation and Policy Stress Simulator

• URL: http://arxiv.org/abs/2607.16275v2
• Date: Tue, 21 Jul 2026 05:02:07 GMT
• Title: QR-SPPS: Quantum-Native Retail Shock Propagation and Policy Stress Simulator
• Abstract summary: We present QR-SPPS, a quantum-native framework for retail supply chain risk analysis.<n>A 40-node, four-tier supply network is mapped to a 40-qubit Hamiltonian with ZZ coupling terms representing correlated supplier dependencies.<n>A hardware-efficient Variational Quantum Eigensolver (VQE) computes the stress ground state, revealing entangled cascade failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Intelligence-Guided Adaptive Purification for DDoS-Resilient Quantum Networks: A CUDA-Q based Study [0.0]
  We study adaptive entanglement purification in quantum-repeater chains.<n>We show that cyber-state awareness can improve useful quantum-network outcomes by trading raw for fidelity-qualified delivery.
  arXiv  Detail & Related papers  (2026-07-08T18:56:51Z)
• QR-SPPS: Quantum-Native Retail Supply Chain Risk Simulation via VQE, ADAPT-VQE Counterfactual Policy Ranking, and DOS-QPE Boltzmann Tail Risk Quantification [0.0]
  We present QR-SPPS (Quantum-Native Retail Shock Propagation and Policy Stress Simulator), a three-algorithm quantum pipeline implemented using the Qiskit framework.<n>First, a 40-node, 4-tier retail supply network is encoded as a 40-qubit Ising Hamiltonian using OpenFermion QubitOperator.<n>Second, a hardware-efficient VQE circuit finds the ground-state stress distribution with zero error.<n>Third, we introduce the first application of ADAPT-VQE screening to counterfactual macroeconomic policy evaluation.
  arXiv  Detail & Related papers  (2026-03-21T07:10:29Z)
• Consensus Protocols for Entanglement-Aware Scheduling in Distributed Quantum Neural Networks [18.148407575244942]
  We introduce a Consensus-Entanglement-Aware Scheduling (CEAS) framework that co-designs quantum consensus protocols with adaptive entanglement management.<n>CEAS integrates fidelity-weighted aggregation, in which parameter updates are weighted by quantum Fisher information to suppress noisy contributions.<n>CEAS maintains 10-15 percentage points higher accuracy compared to entanglement-oblivious baselines under coordinated Byzantine attacks.
  arXiv  Detail & Related papers  (2026-02-06T16:36:52Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era [3.1351527202068445]
  This research offers a broad roadmap for Trustworthy Quantum Machine Learning (TQML)<n>It integrates three foundational pillars of reliability: (i) uncertainty quantification for calibrated and risk-aware decision making, (ii) robustness adversarial against classical and quantum-native threat models, and (iii) privacy preservation in distributed and delegated quantum learning scenarios.<n>This roadmap seeks to define trustworthiness as a first-class design objective for quantum AI.
  arXiv  Detail & Related papers  (2025-11-04T14:24:17Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Bayesian Quantum Amplitude Estimation [46.03321798937855]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networks [62.87033427172205]
  Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly. A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
  arXiv  Detail & Related papers  (2024-01-19T17:16:40Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Scaling Limits of Quantum Repeater Networks [62.75241407271626]
  Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
  arXiv  Detail & Related papers  (2023-05-15T14:57:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.