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Embedding compression can be dramatically improved by learning the optimal coding geometry from data, not just optimizing within a fixed one.
GenQAS is introduced, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay and can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.