# Nonlinear regression based on a hybrid quantum computer

arXiv: https://arxiv.org/abs/1808.09607 · HTML: https://mlsanity.com/paper/1808.09607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning For Classical Data](https://mlsanity.com/paper/2105.03684/) (2021)

## Abstract (arXiv)

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.