Maximising Quantum-Computing Expressive Power through Randomised Circuits
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910445516881920 |
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| author | Yang, Yingli Zhang, Zongkang Wang, Anbang Xu, Xiaosi Wang, Xiaoting Li, Ying |
| author_facet | Yang, Yingli Zhang, Zongkang Wang, Anbang Xu, Xiaosi Wang, Xiaoting Li, Ying |
| contents | In the noisy intermediate-scale quantum era, variational quantum algorithms (VQAs) have emerged as a promising avenue to obtain quantum advantage. However, the success of VQAs depends on the expressive power of parameterised quantum circuits, which is constrained by the limited gate number and the presence of barren plateaus. In this work, we propose and numerically demonstrate a novel approach for VQAs, utilizing randomised quantum circuits to generate the variational wavefunction. We parameterize the distribution function of these random circuits using artificial neural networks and optimize it to find the solution. This random-circuit approach presents a trade-off between the expressive power of the variational wavefunction and time cost, in terms of the sampling cost of quantum circuits. Given a fixed gate number, we can systematically increase the expressive power by extending the quantum-computing time. With a sufficiently large permissible time cost, the variational wavefunction can approximate any quantum state with arbitrary accuracy. Furthermore, we establish explicit relationships between expressive power, time cost, and gate number for variational quantum eigensolvers. These results highlight the promising potential of the random-circuit approach in achieving a high expressive power in quantum computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01947 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Maximising Quantum-Computing Expressive Power through Randomised Circuits Yang, Yingli Zhang, Zongkang Wang, Anbang Xu, Xiaosi Wang, Xiaoting Li, Ying Quantum Physics Machine Learning In the noisy intermediate-scale quantum era, variational quantum algorithms (VQAs) have emerged as a promising avenue to obtain quantum advantage. However, the success of VQAs depends on the expressive power of parameterised quantum circuits, which is constrained by the limited gate number and the presence of barren plateaus. In this work, we propose and numerically demonstrate a novel approach for VQAs, utilizing randomised quantum circuits to generate the variational wavefunction. We parameterize the distribution function of these random circuits using artificial neural networks and optimize it to find the solution. This random-circuit approach presents a trade-off between the expressive power of the variational wavefunction and time cost, in terms of the sampling cost of quantum circuits. Given a fixed gate number, we can systematically increase the expressive power by extending the quantum-computing time. With a sufficiently large permissible time cost, the variational wavefunction can approximate any quantum state with arbitrary accuracy. Furthermore, we establish explicit relationships between expressive power, time cost, and gate number for variational quantum eigensolvers. These results highlight the promising potential of the random-circuit approach in achieving a high expressive power in quantum computing. |
| title | Maximising Quantum-Computing Expressive Power through Randomised Circuits |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2312.01947 |