Neural Auto-designer for Enhanced Quantum Kernels
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Keywords: Quantum machine learning, kernel learning, quantum kernels, feature map, quantum circuit design
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TL;DR: We propose a general quantum kernel design scheme to enhance the performance of kernel learning. This approach is also feasible on Noisy Intermediate-Scale Quantum machines.
Abstract: 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 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.
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 4610
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