Published Paper

Inserted: 8 aug 2024
Last Updated: 8 aug 2024

Journal: Nat. Comput. Sci.
Year: 2021

Abstract:

It is unknown if near-term quantum computers are advantageous for machine learning tasks. In this work, we address this question by trying to understand how powerful and trainable quantum machine learning models are, relative to popular classical neural networks. We propose the effective dimension—a measure that captures these qualities—and prove that it can be used to assess any statistical model’s ability to generalize on new data. Crucially, the effective dimension is a data-dependent measure that also depends on the Fisher information, which allows us to gauge the ability of a model to train. We demonstrate numerically that a class of quantum neural networks is able to achieve a significantly better effective dimension than comparable feedforward networks and train faster, suggesting an advantage for quantum machine learning, which we verify on real quantum hardware.

Download:

• The-power-of-quantum-neural-networks.pdf