论文标题
变量量子近似支撑向量机与推理转移
Variational Quantum Approximate Support Vector Machine with Inference Transfer
论文作者
论文摘要
基于内核的量子分类器是用于复杂数据超线性分类的最实用，最有影响力的量子机学习技术。我们提出了一种变异量子近似支持向量机（VQASVM）算法，该算法证明了即使在NISQ计算机中也可行的量子操作，可以证明经验性的亚季节运行时复杂性。我们在基于云的NISQ机器上使用玩具示例数据集尝试了算法，作为概念证明。我们还在数字上研究了其在标准的虹膜花和MNIST数据集上的性能，以确认实用性和可扩展性。
A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.
