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- W2790388700 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." @default.
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- W2790388700 date "2018-09-10" @default.
- W2790388700 modified "2023-10-15" @default.
- W2790388700 title "Quantum circuit learning" @default.
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- W2790388700 doi "https://doi.org/10.1103/physreva.98.032309" @default.
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