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.
A unified frequency principle for quan- tum and classical machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
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.
-
Spectral methods: crucial for machine learning, natural for quantum computers?
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.