Cautious optimism for deep parameterized quantum circuits
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performance on unseen data changes as the number of trainable parameters increases. Prior works have derived formal generalization guarantees for quantum models, but it is well-known that many such results do not fully characterize generalization behavior in practice. In this work, we show that gradient-based PQCs can exhibit improved performance on unseen data as model size increases, displaying the phenomenon of double descent. This contrasts with the traditional view that larger models lead to degraded generalization...
| Authors | Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto et al. |
|---|---|
| Primary category | quant-ph |
| Published | 23 July 2026 |
| Last updated at source | 5 August 2026 |
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