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arXiv · 1808.09607

Nonlinear regression based on a hybrid quantum computer

Dan-Bo Zhang·Shi-Liang Zhu·Z. D. Wang
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Abstract

Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid quantum computer, exploiting both discrete and continuous variables, for their capacity to encode novel features and efficiency of processing information. We propose encoding schemes that can realize well-known polynomial and Gaussian kernel ridge regressions, with exponentially speed-up regarding to the number of samples.

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Dan-Bo Zhang, Shi-Liang Zhu, Z. D. Wang. 2018-08-29. Nonlinear regression based on a hybrid quantum computer. https://arxiv.org/abs/1808.09607

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