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A simple reconstruction pre-training stage unlocks state-of-the-art one-step generative modeling with 10x less compute.
Even with only uniform state preparation and measurements, or computational basis access, we can fully reconstruct generic 2-local and many common nearest-neighbor Hamiltonians.
Bosonic Gaussian states can be identified with unprecedented accuracy using a novel warm-start strategy combined with non-Gaussian measurements, outperforming classical Gaussian estimation.