Adaptive Observation Cost Control for Variational Quantum Eigensolvers
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arXiv
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| Format: | Preprint |
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2025
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| author | Anders, Christopher J. Nicoli, Kim A. Wu, Bingting Elosegui, Naima Pedrielli, Samuele Funcke, Lena Jansen, Karl Kühn, Stefan Nakajima, Shinichi |
| author_facet | Anders, Christopher J. Nicoli, Kim A. Wu, Bingting Elosegui, Naima Pedrielli, Samuele Funcke, Lena Jansen, Karl Kühn, Stefan Nakajima, Shinichi |
| contents | The objective to be minimized in the variational quantum eigensolver (VQE) has a restricted form, which allows a specialized sequential minimal optimization (SMO) that requires only a few observations in each iteration. However, the SMO iteration is still costly due to the observation noise -- one observation at a point typically requires averaging over hundreds to thousands of repeated quantum measurement shots for achieving a reasonable noise level. In this paper, we propose an adaptive cost control method, named subspace in confident region (SubsCoRe), for SMO. SubsCoRe uses the Gaussian process (GP) surrogate, and requires it to have low uncertainty over the subspace being updated, so that optimization in each iteration is performed with guaranteed accuracy. The adaptive cost control is performed by first setting the required accuracy according to the progress of the optimization, and then choosing the minimum number of measurement shots and their distribution such that the required accuracy is satisfied. We demonstrate that SubsCoRe significantly improves the efficiency of SMO, and outperforms the state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Observation Cost Control for Variational Quantum Eigensolvers Anders, Christopher J. Nicoli, Kim A. Wu, Bingting Elosegui, Naima Pedrielli, Samuele Funcke, Lena Jansen, Karl Kühn, Stefan Nakajima, Shinichi Quantum Physics Machine Learning The objective to be minimized in the variational quantum eigensolver (VQE) has a restricted form, which allows a specialized sequential minimal optimization (SMO) that requires only a few observations in each iteration. However, the SMO iteration is still costly due to the observation noise -- one observation at a point typically requires averaging over hundreds to thousands of repeated quantum measurement shots for achieving a reasonable noise level. In this paper, we propose an adaptive cost control method, named subspace in confident region (SubsCoRe), for SMO. SubsCoRe uses the Gaussian process (GP) surrogate, and requires it to have low uncertainty over the subspace being updated, so that optimization in each iteration is performed with guaranteed accuracy. The adaptive cost control is performed by first setting the required accuracy according to the progress of the optimization, and then choosing the minimum number of measurement shots and their distribution such that the required accuracy is satisfied. We demonstrate that SubsCoRe significantly improves the efficiency of SMO, and outperforms the state-of-the-art methods. |
| title | Adaptive Observation Cost Control for Variational Quantum Eigensolvers |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2502.01704 |