AINeutralarXiv – CS AI · 18h ago6/10
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Repair Before Veto, When Repair Is Hidden: Quantum-Accessible Features for Repair-Augmented Constraint Learning
Researchers introduce Q-RACL, a quantum-enhanced machine learning framework that uses quantum computing to solve a critical constraint satisfaction problem: determining which repairs can restore feasibility to rejected candidates. The system demonstrates quantum advantage in accessing hidden discrete logarithm features that classical algorithms cannot efficiently process, achieving false-veto rates below 1.1% where classical approaches fail.