Quantum circuit learning

作者：Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, Keisuke Fujii · 发表于：Physical Review A · 年份：2018 · DOI：10.1103/physreva.98.032309 · 被引用次数：1682 · 研究领域：Quantum Computing Algorithms and Architecture、Quantum Information and Cryptography、Neural Networks and Reservoir Computing

We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.