Submission Quantum Neural Network Classifiers: A Tutorial

By Weikang Li et al

Published on July 12, 2022

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Table of Contents

1 Introduction
2 Basic concepts
2.1 A recap of quantum computing and quantum classifiers
2.1.1 The basic knowledge of quantum computing
2.1.2 A categorization of quantum classifiers
2.2 The variational circuit structure of QNNs
2.2.1 Amplitude-encoding based QNNs
2.2.2 Block-encoding based QNNs
2.3 Optimization strategies during the training process
3 Amplitude-encoding based QNNs
3.1 The list of variables and caveats
3.2 Benchmarks of the performance
4 Block-encoding based QNNs
4.1 The list of variables and caveats
4.2 Benchmarks of the performance
5 Conclusion and outlooks
A Preparation before running the code
B Complete example codes
References

Summary

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to modern society. Quantum neural networks in the form of parameterized quantum circuits are discussed in this tutorial. Different structures and encoding strategies of quantum neural networks for supervised learning tasks are explored, and their performance is benchmarked utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes provided aim to offer convenience for beginners in scientific works and assist in developing powerful variational quantum learning models.