Physics-anchored residual quantum neural networks for cloud-cover parameterization

Key points

• We investigate an offline residual formulation that retains a fitted \XR\ cloud-cover estimate and learns a correction to its logit in transformed target space.1
• Using released ICON/DYAMOND-derived data, we compare ten model variants in six- and eight-input settings, with three matched replicates and 10,000 training samples per replicate.1
• Under a common 30-epoch protocol, residual prediction reduced physical-space test mean squared error relative to direct QNN prediction by 54.3% and 38.8% for six-input XYZ and ZZXY circuits, and by 49.2% and 38.9% for their eight-input versions.1
• The results support residual prediction for compact simulated QNNs within the tested budget and establish a clear boundary set by stronger classical references.1

Sources (1)

• [1]Physics-anchored residual quantum neural networks for cloud-cover parameterization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 12:33 PM

  Compact quantum neural networks have been explored for cloud-cover parameterization, but their predictive value depends on how physical knowledge is incorporated and which classical models provide the reference.

  We investigate an offline residual formulation that retains a fitted \XR\ cloud-cover estimate and learns a correction to its logit in transformed target space.

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