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arXiv · 1803.00745

Quantum Circuit Learning

Kosuke Mitarai·Makoto Negoro·Masahiro Kitagawa·Keisuke Fujii
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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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Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, Keisuke Fujii. 2018-03-02. Quantum Circuit Learning. https://doi.org/10.1103/physreva.98.032309

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