Bin Yang

Quantum-classical hybrid models can achieve subtle but measurable improvements in stellar light-curve classification over pure classical approaches, even with limited qubit counts.

Learning a residual correction to a physics-informed estimate dramatically boosts QNN performance in cloud-cover parameterization, outperforming both direct QNNs and compact MLPs.

Spatial coding agents can be distilled into standalone MLLMs, achieving superior spatial reasoning and generalization without external tools.

This work proposes QUALS, a large-scale time series corpus equilibrium framework that significantly enhances data efficiency, enabling existing models to achieve superior performance using only a small fraction of the original training data.