Jan 25 – 29, 2019
US/Pacific timezone

Quantum circuit learning: a variational quantum algorithm for machine learning

Jan 26, 2019, 4:30 PM
Building 66- Auditorium (LBL-Hill)

Building 66- Auditorium


Lawrence Berkeley National Lab Berkeley, California
Open-source tools and quantum machine learning Quantum machine learning and quantum sensing


Mr Kosuke Mitarai (Osaka University)


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 to perform a given task by tuning parameters implemented on it. We also provide a way to obtain an analytical gradient of an expectation value of an observable for gradient-beased optimization of parameters. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Quantum circuits can provide feature maps that have not been accessible with classical approach. Hybridizing a low-depth quantum circuit and a classical computer for machine learning.

Primary author

Mr Kosuke Mitarai (Osaka University)


Dr Keisuke Fujii (Kyoto University) Dr Makoto Negoro (Osaka University) Prof. Masahiro Kitagawa (Osaka University)

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