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Aligning inference with the forward statistical structure of diffusion models can dramatically boost generative fidelity, as evidenced by significant performance gains on CIFAR-10 and ImageNet.
Conditioning on informative local measurements transforms a memorization-prone estimator into a high-fidelity quantum state tomography tool, achieving 0.95 fidelity with fewer measurements.
Entangled quantum circuits can significantly hinder generalization, leading to worse performance than non-entangled circuits with the same number of parameters.
