A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning
Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show moderate noise can reduce testing error, a behavior unexplained by weak-noise perturbative error accumulation or strong-noise trainability collapse. Here we develop a statistical learning theory connecting microscopic noise processes to macroscopic learning performance. At its heart is a noise-order purity parameter, derived from a surrogate model analysis, that predicts the noise-induced reduction in model complexity and the consequent reduction in the generalization gap. Noise simultaneously increases prediction bias. Their competition expl...
| Authors | Ziyu Zhang, Zikang Jia, Xiaosong Li et al. |
|---|---|
| Primary category | quant-ph |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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