Physics-anchored residual quantum neural networks for cloud-cover parameterization
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. 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. All quantum circuits use local Z readout and noiseless exact expectation values. Under a common 30-epoch protocol, residual prediction reduced ph...
| Authors | Zhuo Wang, Ce Yu, Bin Yang |
|---|---|
| Primary category | quant-ph |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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