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Quantum Neural Network Classifiers: A Tutorial
by Weikang Li, Zhide Lu, Dong-Ling Deng
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Submission summary
| Authors (as registered SciPost users): | Weikang Li · Zhide Lu |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2206.02806v2 (pdf) |
| Code repository: | https://github.com/LWKJJONAK/Quantum_Neural_Network_Classifiers |
| Data repository: | https://github.com/LWKJJONAK/Quantum_Neural_Network_Classifiers/tree/main/benchmark_data |
| Date accepted: | 2022-07-21 |
| Date submitted: | 2022-07-13 04:23 |
| Submitted by: | Li, Weikang |
| Submitted to: | SciPost Physics Lecture Notes |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
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.
List of changes
1. Following the referee’s suggestion, we added more discussions about the motivations for developing QNN classifiers in the Introduction section.
2. Minor revisions are made (mostly about incorrectly used words).
3. We added a new subsection titled “A recap of quantum computing and quantum classifiers”, including “The basic knowledge of quantum computing” and “A categorization of quantum classifiers”, aiming to provide convenience for readers unfamiliar with this field.
4. We changed the style of the code box for better readability in both the PDF and printed versions.
5. More explanations for some basic concepts.
6. We added a new subsection titled “Effects of finite measurements and experimental noises”.
Published as SciPost Phys. Lect. Notes 61 (2022)