  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.
