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 des
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- PossiblePossibly related (embedding) · 50%Path Integral Quantum Mechanics in the Era of Machine Learning - AIP Publishing LLC →
- LinkedLinked via arxiv author · 85%Marie Kempkes →
“Cautious optimism for deep parameterized quantum circuits”
- LinkedLinked via arxiv author · 85%Elies Gil-Fuster →
“Cautious optimism for deep parameterized quantum circuits”
- LinkedLinked via arxiv author · 85%Carlos Bravo-Prieto →
“Cautious optimism for deep parameterized quantum circuits”
- LinkedLinked via arxiv author · 85%Aroosa Ijaz →
“Cautious optimism for deep parameterized quantum circuits”
- LinkedLinked via arxiv author · 85%Alissa Wilms →
“Cautious optimism for deep parameterized quantum circuits”
- LinkedLinked via arxiv author · 85%Jens Eisert →
“Cautious optimism for deep parameterized quantum circuits”
- LinkedLinked via arxiv author · 85%Evert van Nieuwenburg →
“Cautious optimism for deep parameterized quantum circuits”