A new review paper by Hoong Chuin Lau examines the intersection of AI and quantum computing for combinatorial optimization, categorizing research into three paradigms: AI-for-Quantum Optimization (AI-for-QO), Quantum-for-AI (Q-for-AI), and AI-Quantum Co-Optimization. The study, which analyzed 119 papers, concludes that AI-assisted quantum optimization shows promise, while quantum-assisted AI models are largely unproven. The review emphasizes that scaling quantum optimization is a systems engineering problem requiring selective quantum use, with only a few studies isolating quantum contributions or establishing classical baselines.
The post Review Evaluates AI and Quantum Computing Paradigms for Adaptive Combinatorial Optimization appeared first on Quantum Computing Report.