New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm
Currently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift r...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ada0a4 |
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| author | Xiaodong Ding FuDong Liu Weilong Wang Yu Zhu Yifan Hou Yizhen Huang Jinchen Xu Zheng Shan |
| author_facet | Xiaodong Ding FuDong Liu Weilong Wang Yu Zhu Yifan Hou Yizhen Huang Jinchen Xu Zheng Shan |
| author_sort | Xiaodong Ding |
| collection | DOAJ |
| description | Currently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift rule can efficiently compute the parameter gradient with quantum circuits, it needs to run the quantum circuit twice repeatedly, which significantly reduces the computation efficiency. In order to overcome this challenge, this paper innovatively integrates the principle of unitary operation in quantum mechanics with the technical characteristics of superconducting quantum chips and elaborately designs some new parameterized quantum gates (PQGs). These PQGs strictly follow the rules of unitary operation, which ensures the stability and accuracy of quantum state evolution while realizing an efficient solution to the parameter gradient. Especially for the gradient calculation of a single qubit and single-angle PQGs, the new method can be completed with only a single quantum circuit run, which greatly improves the computation efficiency. Experimental validation on benchmark datasets such as breast cancer and iris shows that the method proposed in this paper exhibits excellent performance on quantum classification tasks. Compared with the parameter-shift rule, the computation efficiency of the new method is improved by 40%. And the classification accuracy, precision, and other key performance metrics are improved by an average of 5% in comparison with traditional optimization algorithms. This work not only enriches the methodology of quantum machine learning theoretically but also demonstrates its remarkable superiority in practical applications, which indicates that the method has great potential in scientific research and industrial applications. |
| format | Article |
| id | doaj-art-ccaf0621bff64dbb8ac38dca76899881 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-ccaf0621bff64dbb8ac38dca768998812025-01-13T07:19:59ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101500410.1088/2632-2153/ada0a4New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithmXiaodong Ding0https://orcid.org/0000-0001-9947-4035FuDong Liu1Weilong Wang2Yu Zhu3Yifan Hou4Yizhen Huang5Jinchen Xu6Zheng Shan7https://orcid.org/0009-0003-9602-0988Laboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaLaboratory for Advanced Computing and Intelligence Engineering , No. 62, Science Avenue, Zhengzhou City, Henan Province, People’s Republic of ChinaCurrently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift rule can efficiently compute the parameter gradient with quantum circuits, it needs to run the quantum circuit twice repeatedly, which significantly reduces the computation efficiency. In order to overcome this challenge, this paper innovatively integrates the principle of unitary operation in quantum mechanics with the technical characteristics of superconducting quantum chips and elaborately designs some new parameterized quantum gates (PQGs). These PQGs strictly follow the rules of unitary operation, which ensures the stability and accuracy of quantum state evolution while realizing an efficient solution to the parameter gradient. Especially for the gradient calculation of a single qubit and single-angle PQGs, the new method can be completed with only a single quantum circuit run, which greatly improves the computation efficiency. Experimental validation on benchmark datasets such as breast cancer and iris shows that the method proposed in this paper exhibits excellent performance on quantum classification tasks. Compared with the parameter-shift rule, the computation efficiency of the new method is improved by 40%. And the classification accuracy, precision, and other key performance metrics are improved by an average of 5% in comparison with traditional optimization algorithms. This work not only enriches the methodology of quantum machine learning theoretically but also demonstrates its remarkable superiority in practical applications, which indicates that the method has great potential in scientific research and industrial applications.https://doi.org/10.1088/2632-2153/ada0a4VQCAsparameters optimizationPQGsquantum–classical hybrid algorithms |
| spellingShingle | Xiaodong Ding FuDong Liu Weilong Wang Yu Zhu Yifan Hou Yizhen Huang Jinchen Xu Zheng Shan New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm Machine Learning: Science and Technology VQCAs parameters optimization PQGs quantum–classical hybrid algorithms |
| title | New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm |
| title_full | New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm |
| title_fullStr | New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm |
| title_full_unstemmed | New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm |
| title_short | New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm |
| title_sort | new parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm |
| topic | VQCAs parameters optimization PQGs quantum–classical hybrid algorithms |
| url | https://doi.org/10.1088/2632-2153/ada0a4 |
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