论文标题
通过Hong-Ou-Mandel干扰评估量子内核
Quantum Kernel Evaluation via Hong-Ou-Mandel Interference
论文作者
论文摘要
量子计算中增长最快的领域之一是它在机器学习方法中的使用，特别是通过应用量子内核。尽管有如此浓厚的兴趣，但相关物理平台评估量子内核的建议很少。在本文中，我们提出并模拟了一种能够使用Hong-Ou-Mandel（HOM）干扰评估量子内核的方案，这是一种实验技术，可用于光学研究人员广泛使用。我们的建议利用单个光子的正交时间模式，允许一个人编码多维特征向量。结果，干扰两个光子并使用检测到的巧合计数，我们可以执行直接测量和二元分类。这个物理平台赋予了指数量子优势，在其他作品中也在理论上描述了。我们介绍了此方法的完整描述，并执行数值实验，以演示用于经典数据二进制分类的样本应用。
One of the fastest growing areas of interest in quantum computing is its use within machine learning methods, in particular through the application of quantum kernels. Despite this large interest, there exist very few proposals for relevant physical platforms to evaluate quantum kernels. In this article, we propose and simulate a protocol capable of evaluating quantum kernels using Hong-Ou-Mandel (HOM) interference, an experimental technique that is widely accessible to optics researchers. Our proposal utilises the orthogonal temporal modes of a single photon, allowing one to encode multi-dimensional feature vectors. As a result, interfering two photons and using the detected coincidence counts, we can perform a direct measurement and binary classification. This physical platform confers an exponential quantum advantage also described theoretically in other works. We present a complete description of this method and perform a numerical experiment to demonstrate a sample application for binary classification of classical data.
