AI RESEARCH
Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
arXiv CS.AI
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ArXi:2605.23138v1 Announce Type: cross Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces.