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GRPO-QPS: Target-Preserving Reinforcement Learning for Quantum Posterior Sampling
Bayesian quantum tomography requires efficient inference while preserving a posterior fixed by the prior and Born likelihood. Learned transport provides fast amortized samples, but reward tuning can reshape the generated distribution rather than improve exploration of this fixed target. We introduce
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