论文标题
使用重量映射来改善量子变量分类器的收敛性
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
论文作者
论文摘要
近年来,Quantum机器学习的使用变化量子电路(VQC)的使用大幅增加。 VQC的灵感来自人工神经网络,该神经网络在广泛的AI任务中获得了非凡的性能,作为大量参数化函数近似值。 VQC已经证明了有希望的结果,例如,通过使用更强大的算法工具箱,在概括和更少的参数训练中的要求。 VQCS的可训练参数或权重通常用作旋转门中的角度,而当前基于梯度的训练方法则无法解释这一点。我们介绍了VQC的重量映射,以明确地将重量映射到长度为$2π$的间隔,从传统ML中汲取灵感,在许多情况下,数据重新缩放或标准化技术在许多情况下都显示出巨大的好处。我们采用了一组五个功能,并以各种分类器为例在虹膜和葡萄酒数据集上对其进行了评估。我们的实验表明,重量映射可以改善所有测试设置的收敛性。此外,我们能够证明使用未修改的重量,重新映射葡萄酒数据集的测试准确性提高了$ 10 \%$。
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a wide range of AI tasks as massively parameterized function approximators. VQCs have already demonstrated promising results, for example, in generalization and the requirement for fewer parameters to train, by utilizing the more robust algorithmic toolbox available in quantum computing. A VQCs' trainable parameters or weights are usually used as angles in rotational gates and current gradient-based training methods do not account for that. We introduce weight re-mapping for VQCs, to unambiguously map the weights to an interval of length $2π$, drawing inspiration from traditional ML, where data rescaling, or normalization techniques have demonstrated tremendous benefits in many circumstances. We employ a set of five functions and evaluate them on the Iris and Wine datasets using variational classifiers as an example. Our experiments show that weight re-mapping can improve convergence in all tested settings. Additionally, we were able to demonstrate that weight re-mapping increased test accuracy for the Wine dataset by $10\%$ over using unmodified weights.