Benchmarking Quantum Kernels Across Diverse and Complex Data

• URL: http://arxiv.org/abs/2511.10831v1
• Date: Thu, 13 Nov 2025 22:20:20 GMT
• Title: Benchmarking Quantum Kernels Across Diverse and Complex Data
• Authors: Yuhan Jiang, Matthew Otten,
• Abstract summary: We develop a variational quantum kernel framework for complex classification tasks.<n>We conduct a benchmark on eight challenging, real world and high-dimensional datasets.<n>This work demonstrates that properly designed quantum kernels can function as versatile, high-performance tools.
• Score: 2.118455551237009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum kernel methods are a promising branch of quantum machine learning, yet their practical advantage on diverse, high-dimensional, real-world data remains unverified. Current research has largely been limited to low-dimensional or synthetic datasets, preventing a thorough evaluation of their potential. To address this gap, we developed a variational quantum kernel framework utilizing resource-efficient ansätze for complex classification tasks and introduced a parameter scaling technique to accelerate convergence. We conducted a comprehensive benchmark of this framework on eight challenging, real world and high-dimensional datasets covering tabular, image, time series, and graph data. Our classically simulated results show that the proposed quantum kernel demonstrated a clear performance advantage over standard classical kernels, such as the radial basis function (RBF) kernel. This work demonstrates that properly designed quantum kernels can function as versatile, high-performance tools, laying a foundation for quantum-enhanced applications in real-world machine learning. Further research is needed to fully assess the practical quantum advantage.

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