Understanding quantum machine learning also requires rethinking generalization.

ABSTRACT: Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization fail to explain the behavior of such quantum models. Our experiments reveal that state-of-the-art quantum neural networks accurately fit random states and random labeling of training data. This ability to memorize random data defies current notions of small generalization error, problematizing approaches that build on complexity measures such as the VC dimension, the Rademacher complexity, and all their uniform relatives. We complement our empirical results with a theoretical construction showing that quantum neural networks can fit arbitrary labels to

SUBMITTER: Gil-Fuster E 

PROVIDER: S-EPMC10938005 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature