# Quantum Circuit Learning

**Authors:** Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, Keisuke Fujii  
**arXiv:** [1803.00745](https://arxiv.org/abs/1803.00745)  
**Date:** 2018-03-02  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/1803.00745v3/

## 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.