提出高效量子核方法，在真实高维数据上表现媲美经典核模型。
A Versatile Variational Quantum Kernel Framework for Non-Trivial Classification
- 采用资源高效电路设计量子核，适配复杂分类任务
- 在8个真实高维数据集上达到与RBF核相当的准确率
- 适合对量子机器学习性能评估感兴趣的科研人员
量子核方法是量子机器学习中极具潜力的方向，但其在多样化、高维真实数据上的有效性尚未验证。当前研究多局限于低维或合成数据集，难以全面评估其潜力。为此，我们提出一种基于资源高效参数化电路的可变量子核算法框架，并引入参数缩放技术以加速收敛。我们在八个具有挑战性的真实世界高维数据集（涵盖表格、图像、时间序列和图数据）上进行了全面基准测试。结果表明，所提出的量子核在经典模拟中表现出与标准经典核（如径向基函数核）相当的分类精度。该工作证明，合理设计的量子核可作为通用且高性能的工具，为量子增强型实际机器学习应用奠定基础。未来仍需进一步评估量子方法的实际性能。
原文摘要 · Abstract (English)
Quantum kernel methods are a promising branch of quantum machine learning, yet their effectiveness 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 an algorithmic framework for variational quantum kernels 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 results show that the proposed quantum kernels demonstrate competitive classification accuracy compared to standard classical kernels in classical simulation, 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 performance of quantum methods.
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