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quant-ph2026

Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction

Aditya Kumar, Sumit Chongder

Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-…

quant-ph2026

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

Sumit Tapas Chongder

Classical supply chain risk models treat node failures as statistically independent events, systematically underestimating correlated cascade failures across multi-tier supplier ne…

quant-ph2026

Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code

Sumit Chongder

Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing. We pres…

quant-ph2026

Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems

Sumit Chongder

Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightwei…

quant-ph2026

QR-SPPS: Quantum-Native Retail Supply Chain Risk Simulation via VQE, ADAPT-VQE Counterfactual Policy Ranking, and DOS-QPE Boltzmann Tail Risk Quantification

Sumit Tapas Chongder

Classical supply chain risk models treat node failures as statistically independent events, systematically underestimating cascade probabilities when supplier dependencies are stro…