Barren plateaus are amplified by the dimension of qudits
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910914594209792 |
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| author | Friedrich, Lucas Farias, Tiago de Souza Maziero, Jonas |
| author_facet | Friedrich, Lucas Farias, Tiago de Souza Maziero, Jonas |
| contents | Variational Quantum Algorithms (VQAs) have emerged as pivotal strategies for attaining quantum advantage in diverse scientific and technological domains, notably within Quantum Neural Networks. However, despite their potential, VQAs encounter significant obstacles, chief among them being the vanishing gradient problem, commonly referred to as barren plateaus. In this article, through meticulous analysis, we demonstrate that existing literature implicitly suggests the intrinsic influence of qudit dimensionality on barren plateaus. To instantiate these findings, we present numerical results that exemplify the impact of qudit dimensionality on barren plateaus. Therefore, despite the proposition of various error mitigation techniques, our results call for further scrutiny about their efficacy in the context of VQAs with qudits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Barren plateaus are amplified by the dimension of qudits Friedrich, Lucas Farias, Tiago de Souza Maziero, Jonas Quantum Physics Machine Learning Variational Quantum Algorithms (VQAs) have emerged as pivotal strategies for attaining quantum advantage in diverse scientific and technological domains, notably within Quantum Neural Networks. However, despite their potential, VQAs encounter significant obstacles, chief among them being the vanishing gradient problem, commonly referred to as barren plateaus. In this article, through meticulous analysis, we demonstrate that existing literature implicitly suggests the intrinsic influence of qudit dimensionality on barren plateaus. To instantiate these findings, we present numerical results that exemplify the impact of qudit dimensionality on barren plateaus. Therefore, despite the proposition of various error mitigation techniques, our results call for further scrutiny about their efficacy in the context of VQAs with qudits. |
| title | Barren plateaus are amplified by the dimension of qudits |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2405.08190 |