Quantum machine learning with quantum kernels for classification problems is
a growing area of research. Recently, quantum kernel alignment techniques that
parameterise the kernel have been developed, allowing the kernel to be trained
and therefore aligned with a specific dataset. While quantum kernel alignment
is a promising technique, it has been hampered by considerable training costs
because the full kernel matrix must be constructed at every training iteration.
Addressing this challenge, we introduce a novel method that seeks to balance
efficiency and performance. We present a sub-sampling training approach that
uses a subset of the kernel matrix at each training step, thereby reducing the
overall computational cost of the training. In this work, we apply the
sub-sampling method to synthetic datasets and a real-world breast cancer
dataset and demonstrate considerable reductions in the number of circuits
required to train the quantum kernel while maintaining classification accuracy.

使用量子核对分类问题进行量子机器学习是一个正在快速发展的研究领域。我们提出了一种新的子采样训练方法，通过在每个训练步骤中使用核矩阵的子集，从而降低了训练的整体计算成本，并展示了在维持分类准确性的同时大幅减少了训练量子核所需电路数量的能力。