Empirical scaling study finds dataset-dependent performance saturation and quantum metric trends in hybrid QNN classifiers as depth and width vary.
Reconciling modern machine-learning practice and the classical bias–variance trade-off,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics
Empirical scaling study finds dataset-dependent performance saturation and quantum metric trends in hybrid QNN classifiers as depth and width vary.