利用混合自动编码器提升变分量子分类器的性能
Enhancing the performance of Variational Quantum Classifiers with hybrid autoencoders
AI总结：
针对VQC难以处理高维数据的问题，提出结合后续量子嵌入的定向降维方法及量子启发经典自动编码器，实验证明其可提升分类性能并降低重构损失。
摘要：
变分量子电路（VQC）处于量子机器学习研究的前沿。尽管如此，若采用通常的角度编码方案，由于可用量子比特的数量无法容纳高维数据，将量子网络用于真实数据处理仍具挑战性。为实现降维，在将经典特征嵌入量子比特之前，主成分分析通常被用作预处理方法。在本研究中，我们提出一种替代方法，通过考虑其后特定的量子嵌入来降低给定数据集的维度。该方法致力于使采用VQC的量子机器学习在高维数据集上更具通用性和有效性。在第二步中，我们提出一种量子启发的经典自动编码器模型，可用于在低维潜在空间中编码信息。我们通过数值测试展示了所提模型的能力。结果表明，我们的定向降维方法显著提升了VQC的性能，并且我们还识别出第二种模型在重构损失方面优于经典线性自动编码器的情形。
英文摘要：
Variational Quantum Circuits (VQC) lie at the forefront of quantum machine learning research. Still, the use of quantum networks for real data processing remains challenging as the number of available qubits cannot accommodate a large dimensionality of data --if the usual angle encoding scenario is used. To achieve dimensionality reduction, Principal Component Analysis is routinely applied as a pre-processing method before the embedding of the classical features on qubits. In this work, we propose an alternative method which reduces the dimensionality of a given dataset by taking into account the specific quantum embedding that comes after. This method aspires to make quantum machine learning with VQCs more versatile and effective on datasets of high dimension. At a second step, we propose a quantum inspired classical autoencoder model which can be used to encode information in low latent spaces. The power of our proposed models is exhibited via numerical tests. We show that our targeted dimensionality reduction method considerably boosts VQC's performance and we also identify cases for which the second model outperforms classical linear autoencoders in terms of reconstruction loss.
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