Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning
Quantum machine learning models based on parameterized circuits can be viewed as Fourier series approximators. However, they often struggle to learn functions with multiple frequency components, particularly high-frequency or non-dominant ones; a phenomenon we term the quantum Fourier parameterization bias. Inspired by recent advances in classical Fourier neural operators (FNOs), we adapt the multi-stage residual learning idea to the quantum domain, iteratively training additional quantum modules on the residuals of previous stages. We evaluate our method on a synthetic benchmark composed of spatially localized frequency components with diverse envelope shapes (Gaussian, Lorentzian, trian...
| Authors | Ammar Daskin |
|---|---|
| Primary category | quant-ph |
| Published | 10 March 2026 |
| Last updated at source | 5 May 2026 |
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