Quantum Circuit Learning

2018/03/31 by Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa +1 · 12 citations
Physics and Astronomy · #quant-ph

paper · pdf · doi:10.1103/physreva.98.032309

published as Phys. Rev. A 98, 032309 (2018)

arxiv created 2019/04/24 · arxiv updated 2019/04/25

Abstract

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.

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