Mitigating Barren Plateaus of Variational Quantum Eigensolvers
Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the expressibility of the ansatzes and is seriously limited by optimization issues, such as barr...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10485449/ |
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| author | Xia Liu Geng Liu Hao-Kai Zhang Jiaxin Huang Xin Wang |
| author_facet | Xia Liu Geng Liu Hao-Kai Zhang Jiaxin Huang Xin Wang |
| author_sort | Xia Liu |
| collection | DOAJ |
| description | Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the expressibility of the ansatzes and is seriously limited by optimization issues, such as barren plateaus (i.e., vanishing gradients). This article proposes the state-efficient ansatz (SEA) for accurate ground state preparation with improved trainability. We show that the SEA can generate an arbitrary pure state with much fewer parameters than a universal ansatz, making it efficient for tasks like ground state estimation. Then, we prove that barren plateaus can be efficiently mitigated by the SEA and the trainability can be further improved most quadratically by flexibly adjusting the entangling capability of the SEA. Finally, we investigate a plethora of examples in ground state estimation where we obtain significant improvements in the magnitude of the cost gradient and the convergence speed. |
| format | Article |
| id | doaj-art-262e7f03d828464787f7162fdfa1875d |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-262e7f03d828464787f7162fdfa1875d2025-01-25T00:03:46ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511910.1109/TQE.2024.338305010485449Mitigating Barren Plateaus of Variational Quantum EigensolversXia Liu0https://orcid.org/0000-0003-4279-5449Geng Liu1https://orcid.org/0000-0003-3838-7530Hao-Kai Zhang2Jiaxin Huang3https://orcid.org/0000-0002-4036-3741Xin Wang4https://orcid.org/0000-0002-0641-3186Thrust of Artificial Intelligence, Information Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, ChinaInstitute for Quantum Computing, Baidu Research, Beijing, ChinaInstitute for Quantum Computing, Baidu Research, Beijing, ChinaInstitute for Quantum Computing, Baidu Research, Beijing, ChinaThrust of Artificial Intelligence, Information Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, ChinaVariational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the expressibility of the ansatzes and is seriously limited by optimization issues, such as barren plateaus (i.e., vanishing gradients). This article proposes the state-efficient ansatz (SEA) for accurate ground state preparation with improved trainability. We show that the SEA can generate an arbitrary pure state with much fewer parameters than a universal ansatz, making it efficient for tasks like ground state estimation. Then, we prove that barren plateaus can be efficiently mitigated by the SEA and the trainability can be further improved most quadratically by flexibly adjusting the entangling capability of the SEA. Finally, we investigate a plethora of examples in ground state estimation where we obtain significant improvements in the magnitude of the cost gradient and the convergence speed.https://ieeexplore.ieee.org/document/10485449/Barren plateausnear-term quantum computingparameterized quantum circuits (PQCs)quantum algorithmsquantum simulationvariational quantum eigensolvers (VQEs) |
| spellingShingle | Xia Liu Geng Liu Hao-Kai Zhang Jiaxin Huang Xin Wang Mitigating Barren Plateaus of Variational Quantum Eigensolvers IEEE Transactions on Quantum Engineering Barren plateaus near-term quantum computing parameterized quantum circuits (PQCs) quantum algorithms quantum simulation variational quantum eigensolvers (VQEs) |
| title | Mitigating Barren Plateaus of Variational Quantum Eigensolvers |
| title_full | Mitigating Barren Plateaus of Variational Quantum Eigensolvers |
| title_fullStr | Mitigating Barren Plateaus of Variational Quantum Eigensolvers |
| title_full_unstemmed | Mitigating Barren Plateaus of Variational Quantum Eigensolvers |
| title_short | Mitigating Barren Plateaus of Variational Quantum Eigensolvers |
| title_sort | mitigating barren plateaus of variational quantum eigensolvers |
| topic | Barren plateaus near-term quantum computing parameterized quantum circuits (PQCs) quantum algorithms quantum simulation variational quantum eigensolvers (VQEs) |
| url | https://ieeexplore.ieee.org/document/10485449/ |
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