Quantum Neural Network Classifiers: A Tutorial

TL;DR

This tutorial explores quantum neural network classifiers, discussing their structures, encoding strategies, and benchmarking performance using quantum simulation tools to facilitate research and practical applications in quantum machine learning.

Contribution

It provides a comprehensive overview of quantum neural network architectures and benchmarking methods, with practical code implementations for beginners.

Findings

Benchmark results of quantum neural networks using Yao.jl

Discussion on various encoding strategies for quantum neural networks

Provision of efficient code for developing variational quantum models

Abstract

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.