AI RESEARCH
Quantum End-to-End Learning for Contextual Combinatorial Optimization
arXiv CS.LG
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ArXi:2605.20222v1 Announce Type: cross Contextual combinatorial optimization (CCO) plays a critical role in decision-making under uncertainty, yet remains a significant challenge. We present Quantum End-to-End Learning (QEL), the first quantum computing-based end-to-end learning framework for CCO that leverages Quantum Approximate Optimization Algorithms.