# 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/en/arxiv-id/1803.00745v3/

## Key points

- Paper introduces quantum circuit learning for machine learning on near-term quantum processors
- Quantum circuit learning uses a low-depth quantum circuit to optimize embedded parameters and learn specific tasks
- Effectiveness of using low-depth quantum circuits to approximate nonlinear functions demonstrated through theoretical investigations and numerical simulations
- Potential of leveraging quantum computing techniques in machine learning applications highlighted
- Combination of classical computing resources with low-depth quantum circuits yields promising results and advances quantum machine learning methodologies
- Represents a significant step towards harnessing the power of quantum technologies to enhance machine learning capabilities and tackle complex computational tasks

## Summary

The paper "Quantum Circuit Learning" by Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii introduces a novel classical-quantum hybrid algorithm for machine learning on near-term quantum processors. This framework, known as quantum circuit learning, utilizes a quantum circuit to learn specific tasks by optimizing embedded parameters. Through theoretical investigations and numerical simulations, the authors demonstrate the effectiveness of using a low-depth quantum circuit to approximate nonlinear functions. This highlights the potential of leveraging quantum computing techniques in machine learning applications. By combining classical computing resources with a low-depth quantum circuit, this approach not only yields promising results but also paves the way for utilizing near-term quantum devices in advancing quantum machine learning methodologies. Overall, this work represents a significant step towards harnessing the power of quantum technologies to enhance machine learning capabilities and opens up new possibilities for tackling complex computational tasks.