量子线路实现支持向量机，用量子退火优化模型，最高达90%准确率。
Exploring an implementation of quantum learning pipeline for support vector machines
- 用量子门电路构造核函数，结合量子退火求解优化问题。
- 在合适正则化下，最佳模型F1得分达到90%。
- 适合对量子机器学习和混合量子计算感兴趣的读者。
本文提出一种全量子支持向量机（SVM）学习方案，将基于门的量子核方法与量子退火优化相结合。我们通过多种特征映射和比特配置构建量子核，并利用核-目标对齐（KTA）评估其适用性。将SVM对偶问题重构成无约束二次布尔优化（QUBO）问题，从而可用量子退火器求解。实验表明，高核对齐度与合适的正则化参数可带来优异性能，最佳模型达到90% F1得分。结果验证了端到端量子学习流程的可行性，展现了混合量子架构在量子高性能计算（QHPC）中的潜力。
原文摘要 · Abstract (English)
This work presents a fully quantum approach to support vector machine (SVM) learning by integrating gate-based quantum kernel methods with quantum annealing-based optimization. We explore the construction of quantum kernels using various feature maps and qubit configurations, evaluating their suitability through Kernel-Target Alignment (KTA). The SVM dual problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling its solution via quantum annealers. Our experiments demonstrate that a high degree of alignment in the kernel and an appropriate regularization parameter lead to competitive performance, with the best model achieving an F1-score of 90%. These results highlight the feasibility of an end-to-end quantum learning pipeline and the potential of hybrid quantum architectures in quantum high-performance computing (QHPC) contexts.
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