The power of quantum neural networks
Bulletin of the American Physical Society(2021)
摘要
It is unknown whether 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 in relation 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 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 considerably better effective dimension than comparable feedforward networks and train faster, suggesting an advantage for quantum machine learning, which we verify on real quantum hardware.
更多查看译文
关键词
quantum,neural networks,power
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络