Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promising outcomes shown by VQCs, such as improved generalization and reduced parameter training requirements, are attributed to the robust algorithmic capabilities of quantum computing. However, the current gradient-based training approaches for VQCs do not adequately accommodate the fact that trainable parameters (or weights) are typically used as angles in rotational gates. To address this, we extend the concept of weight re-mapping for VQCs, as introduced by K\"olle et al. (2023). This approach unambiguously maps the weights to an interval of length $2\pi$, mirroring data rescaling techniques in conventional machine learning that have proven to be highly beneficial in numerous scenarios. In our study, we employ seven distinct weight re-mapping functions to assess their impact on eight classification datasets, using variational classifiers as a representative example. Our results indicate that weight re-mapping can enhance the convergence speed of the VQC. We assess the efficacy of various re-mapping functions across all datasets and measure their influence on the VQC's average performance. Our findings indicate that weight re-mapping not only consistently accelerates the convergence of VQCs, regardless of the specific re-mapping function employed, but also significantly increases accuracy in certain cases.

翻译：受人工神经网络在众多人工智能任务中取得显著成功的启发，变分量子电路（VQCs）近期在量子机器学习应用中呈现激增态势。VQCs展现的令人鼓舞的成果（如泛化能力提升与参数训练需求降低），归功于量子计算强大的算法能力。然而，当前基于梯度的VQC训练方法未能充分适应可训练参数（或称权重）通常用作旋转门角度的特性。为此，我们扩展了Kölle等人（2023）提出的VQC权重重映射概念。该方法将权重明确映射到长度为$2\pi$的区间，类似于传统机器学习中已被证明在多种场景下极具价值的数据重缩放技术。在本研究中，我们采用七种不同的权重重映射函数，以变分分类器为典型案例，评估其对八个分类数据集的影响。结果表明，权重重映射可提升VQC的收敛速度。我们评估了各种重映射函数在所有数据集上的有效性，并测量其对VQC平均性能的影响。研究发现，无论采用何种具体重映射函数，权重重映射不仅始终能加速VQC收敛，而且在某些情况下能够显著提高准确率。