Benchmarking Quantum Kernels Across Diverse and Complex Data

2025-11-13

AI Summary

The practical advantages of quantum kernel methods on high-dimensional real-world data remain empirically unvalidated, as existing studies are largely confined to low-dimensional or synthetic benchmarks. Method: We propose a resource-efficient variational quantum kernel framework that integrates parameterized quantum kernel functions, lightweight variational quantum circuits, and a parameter-scaling strategy—enabling effective classification across multimodal high-dimensional data, including images, time series, and graphs. Contribution/Results: Comprehensive evaluation across eight real-world datasets demonstrates that our quantum kernel consistently and significantly outperforms the classical RBF kernel across all tasks—the first empirical validation of quantum kernel superiority in complex, realistic scenarios. This work overcomes key practical bottlenecks in quantum kernel deployment and establishes a scalable, reproducible benchmarking framework and technical pathway for quantum-enhanced machine learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements

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 ans""atze 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.

Problem

• Evaluating quantum kernel performance on diverse real-world datasets
• Developing efficient quantum framework for complex classification tasks
• Benchmarking quantum kernels against classical machine learning methods

Innovation

• Developed variational quantum kernel framework for complex classification
• Introduced parameter scaling technique to accelerate convergence
• Benchmarked quantum kernels on diverse high-dimensional real-world datasets