DiffQEC is a diffusion-based generative decoder that improves logical error rates by up to 10.2% over minimum-weight perfect matching on Google's experimental quantum processor data and supplies posterior error distributions.
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A multivariate diffusion generative downscaling method preserves inter-variable correlations in climate data under large resolution increases, enabling more accurate compound risk assessment.
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DiffQEC: A versatile diffusion model for quantum error correction
DiffQEC is a diffusion-based generative decoder that improves logical error rates by up to 10.2% over minimum-weight perfect matching on Google's experimental quantum processor data and supplies posterior error distributions.
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Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies
A multivariate diffusion generative downscaling method preserves inter-variable correlations in climate data under large resolution increases, enabling more accurate compound risk assessment.