Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
Explainable outlier detection: What, for whom and why?Machine Learning with Applications, 6:100172, 2021
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A reference architecture for continuous software quality intelligence integrates AI-driven requirement mining, risk-based testing, defect prediction, and production feedback in a closed loop, showing reduced defect leakage and faster testing on semi-synthetic data.
citing papers explorer
-
Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
-
AI-Augmented Closed-Loop Quality Engineering: A Reference Architecture for Continuous Software Quality Intelligence
A reference architecture for continuous software quality intelligence integrates AI-driven requirement mining, risk-based testing, defect prediction, and production feedback in a closed loop, showing reduced defect leakage and faster testing on semi-synthetic data.