Computational reach of machine learning models is independent of untrained quantum resources

2026-10-23 · arXiv Quantum Physics

arXiv:2309.13967v4 Announce Type: replace

Abstract: This paper proves that if an untrained quantum circuit is used as a resource in a machine learning model, then on average no resource quantum circuit is better than any other that can achieve the same set of computational effects. The paper also proves a supporting theorem that even if certain mild idealisations are omitted, the average quantum advantage remains negligible at best. These results cast serious doubt on several proposals to use untrained quantum circuits in machine learning models: at best such claims should be substantiated empirically, as this paper proves there is no a priori theoretical reason to suppose that introducing an untrained quantum circuit will increase performance.

原文链接：https://arxiv.org/abs/2309.13967

查看更多资讯请见 AI 行业资讯。