Do Quantum Neural Networks have Simplicity Bias?
This work addresses the fundamental question of whether QNNs can outperform classical DNNs for machine learning practitioners, concluding they generally do not due to inherent limitations.
The paper investigates whether quantum neural networks (QNNs) exhibit simplicity bias, an inductive bias that helps deep neural networks generalize well, and finds that QNNs face a bias-expressivity tradeoff: they can achieve simplicity bias only with limited expressivity, or high expressivity with poor bias, resulting in worse generalization than DNNs.
One hypothesis for the success of deep neural networks (DNNs) is that they are highly expressive, which enables them to be applied to many problems, and they have a strong inductive bias towards solutions that are simple, known as simplicity bias, which allows them to generalise well on unseen data because most real-world data is structured (i.e. simple). In this work, we explore the inductive bias and expressivity of quantum neural networks (QNNs), which gives us a way to compare their performance to those of DNNs. Our results show that it is possible to have simplicity bias with certain QNNs, but we prove that this type of QNN limits the expressivity of the QNN. We also show that it is possible to have QNNs with high expressivity, but they either have no inductive bias or a poor inductive bias and result in a worse generalisation performance compared to DNNs. We demonstrate that an artificial (restricted) inductive bias can be produced by intentionally restricting the expressivity of a QNN. Our results suggest a bias-expressivity tradeoff. Our conclusion is that the QNNs we studied can not generally offer an advantage over DNNs, because these QNNs either have a poor inductive bias or poor expressivity compared to DNNs.