The authors propose a regularization strategy to improve the trainability of variational quantum circuits (VQCs) in the era of noisy intermediate-scale quantum (NISQ) devices. The strategy integrates two key mechanisms:
Leveraging prior knowledge of the training data to regularize the initial distribution of model parameters. This helps mitigate barren plateau issues, where the gradient variance exponentially decreases as the model size increases.
Diffusing Gaussian noise on the model parameters during training. This increases the volatility of the optimization process, helping the model avoid being trapped in saddle points.
The authors conduct extensive ablation studies across four public datasets - Iris, Wine, Titanic, and MNIST. The results demonstrate that:
Overall, the proposed regularization strategy, which combines prior knowledge and Gaussian noise diffusion, can significantly improve the trainability of VQCs compared to baseline methods.
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by Jun Zhuang,J... at arxiv.org 05-06-2024
https://arxiv.org/pdf/2405.01606.pdfDeeper Inquiries