Quantum Neural Network Classifiers: A Tutorial
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 quant...
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| Format: | Article |
| Language: | English |
| Published: |
SciPost
2022-08-01
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| Series: | SciPost Physics Lecture Notes |
| Online Access: | https://scipost.org/SciPostPhysLectNotes.61 |
| _version_ | 1798037273194790912 |
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| author | Weikang Li, Zhide Lu, Dong-Ling Deng |
| author_facet | Weikang Li, Zhide Lu, Dong-Ling Deng |
| author_sort | Weikang Li, Zhide Lu, Dong-Ling Deng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:24:21Z |
| format | Article |
| id | doaj.art-c49ae3f394a44ecea75fb24edb50f204 |
| institution | Directory Open Access Journal |
| issn | 2590-1990 |
| language | English |
| last_indexed | 2024-04-11T21:24:21Z |
| publishDate | 2022-08-01 |
| publisher | SciPost |
| record_format | Article |
| series | SciPost Physics Lecture Notes |
| spelling | doaj.art-c49ae3f394a44ecea75fb24edb50f2042022-12-22T04:02:29ZengSciPostSciPost Physics Lecture Notes2590-19902022-08-016110.21468/SciPostPhysLectNotes.61Quantum Neural Network Classifiers: A TutorialWeikang Li, Zhide Lu, Dong-Ling DengMachine 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.https://scipost.org/SciPostPhysLectNotes.61 |
| spellingShingle | Weikang Li, Zhide Lu, Dong-Ling Deng Quantum Neural Network Classifiers: A Tutorial SciPost Physics Lecture Notes |
| title | Quantum Neural Network Classifiers: A Tutorial |
| title_full | Quantum Neural Network Classifiers: A Tutorial |
| title_fullStr | Quantum Neural Network Classifiers: A Tutorial |
| title_full_unstemmed | Quantum Neural Network Classifiers: A Tutorial |
| title_short | Quantum Neural Network Classifiers: A Tutorial |
| title_sort | quantum neural network classifiers a tutorial |
| url | https://scipost.org/SciPostPhysLectNotes.61 |
| work_keys_str_mv | AT weikanglizhideludonglingdeng quantumneuralnetworkclassifiersatutorial |