Quantum kernels hold great promise for offering computational advantages over
classical learners, with the effectiveness of these kernels closely tied to the
design of the quantum feature map. However, the challenge of designing
effective quantum feature maps for real-world datasets, particularly in the
absence of sufficient prior information, remains a significant obstacle. In
this study, we present a data-driven approach that automates the design of
problem-specific quantum feature maps. Our approach leverages feature-selection
techniques to handle high-dimensional data on near-term quantum machines with
limited qubits, and incorporates a deep neural predictor to efficiently
evaluate the performance of various candidate quantum kernels. Through
extensive numerical simulations on different datasets, we demonstrate the
superiority of our proposal over prior methods, especially for the capability
of eliminating the kernel concentration issue and identifying the feature map
with prediction advantages. Our work not only unlocks the potential of quantum
kernels for enhancing real-world tasks but also highlights the substantial role
of deep learning in advancing quantum machine learning.

我们提出了一种数据驱动的方法，自动设计针对特定问题的量子特征映射，利用特征选择技术处理高维数据，通过深度神经预测器评估不同候选量子核的性能，通过广泛的数值模拟展示了我们方法的优越性，尤其是在消除核浓度问题和确定具有预测优势的特征映射方面。我们的工作不仅释放了量子内核增强实际任务的潜力，还强调了深度学习在推动量子机器学习方面的重要作用。