Barren plateaus are amplified by the dimension of qudits
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 58 references that are in the map. First posted on arXiv 13 May 2024.
Builds on
21 of 58 references.
References · 21
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9 more
- 2023Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracycited by 37
- 2021Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray imagescited by 16
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- 2024Quantum neural network with ensemble learning to mitigate barren plateaus and cost function concentrationcited by 2
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2 papers.