Introducing the Kernel Descent Optimizer for Variational Quantum Algorithms
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912762768130048 |
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| author | Simon, Lars Eble, Holger Radons, Manuel |
| author_facet | Simon, Lars Eble, Holger Radons, Manuel |
| contents | In recent years, variational quantum algorithms have garnered significant attention as a candidate approach for near-term quantum advantage using noisy intermediate-scale quantum (NISQ) devices. In this article we introduce kernel descent, a novel algorithm for minimizing the functions underlying variational quantum algorithms. We compare kernel descent to existing methods and carry out extensive experiments to demonstrate its effectiveness. In particular, we showcase scenarios in which kernel descent outperforms gradient descent and quantum analytic descent. The algorithm follows the well-established scheme of iteratively computing classical local approximations to the objective function and subsequently executing several classical optimization steps with respect to the former. Kernel descent sets itself apart with its employment of reproducing kernel Hilbert space techniques in the construction of the local approximations, which leads to the observed advantages. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_10257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Introducing the Kernel Descent Optimizer for Variational Quantum Algorithms Simon, Lars Eble, Holger Radons, Manuel Quantum Physics Numerical Analysis Functional Analysis 68T99, 90C53, 49J40, 65K10, 65D05, 46N50 In recent years, variational quantum algorithms have garnered significant attention as a candidate approach for near-term quantum advantage using noisy intermediate-scale quantum (NISQ) devices. In this article we introduce kernel descent, a novel algorithm for minimizing the functions underlying variational quantum algorithms. We compare kernel descent to existing methods and carry out extensive experiments to demonstrate its effectiveness. In particular, we showcase scenarios in which kernel descent outperforms gradient descent and quantum analytic descent. The algorithm follows the well-established scheme of iteratively computing classical local approximations to the objective function and subsequently executing several classical optimization steps with respect to the former. Kernel descent sets itself apart with its employment of reproducing kernel Hilbert space techniques in the construction of the local approximations, which leads to the observed advantages. |
| title | Introducing the Kernel Descent Optimizer for Variational Quantum Algorithms |
| topic | Quantum Physics Numerical Analysis Functional Analysis 68T99, 90C53, 49J40, 65K10, 65D05, 46N50 |
| url | https://arxiv.org/abs/2409.10257 |