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DreamQAS uses learned world model to cut VQE calls in quantum architecture search

DreamQAS targets reinforcement-learning-based quantum architecture search, where a variational quantum eigensolver is run again after every added gate even though the circuit transitions and legal actions are already known. The method keeps those exact dynamics and instead learns a decision-useful world model, reducing the number of VQE evaluations needed during search. It is a research preprint posted to arXiv.

quantum-computing/reinforcement-learning/world-models/arxiv