Multi-stage residual learning in quantum circuits mitigates frequency parameterization bias and improves test MSE on synthetic benchmarks with multiple localized frequency components compared to single-stage training.
Effect of data encoding on the expressive power of varia- tional quantum-machine-learning models,
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
-
Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning
Multi-stage residual learning in quantum circuits mitigates frequency parameterization bias and improves test MSE on synthetic benchmarks with multiple localized frequency components compared to single-stage training.