Encoding-dependent generalization bounds for parametrized quantum circuits

Understand

A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization.

• In particular, theoretical guarantees on the out-of-sample performance of such models, in terms of generalization bounds, have emerged.
• However, none of these generalization bounds depend explicitly on how the classical input data is encoded into the PQC.
• We derive generalization bounds for PQC-based models that depend explicitly on the strategy used for data-encoding.