论文标题
支持向量机的量子核的量子电路的组成优化
Compositional optimization of quantum circuits for quantum kernels of support vector machines
论文作者
论文摘要
虽然量子机学习（ML）已被认为是量子计算最有希望的应用之一，但如何构建优于经典ML的量子ML模型仍然是一个主要的开放问题。在这里，我们演示了一种用于构造量子内核的贝叶斯算法，用于将量子门序列适应数据的量子机。该算法通过在贝叶斯信息准则中选择的量子门作为电路选择度量和贝叶斯对所识别的局部最佳量子电路的参数的优化，从而逐渐增加量子电路的复杂性。目的是为SVM构建量子内核，以尽可能少的培训数据解决分类问题。此处考虑的分类问题所得量子模型的性能大大超过了具有常规内核的优化经典模型。
While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question. Here, we demonstrate a Bayesian algorithm for constructing quantum kernels for support vector machines that adapts quantum gate sequences to data. The algorithm increases the complexity of quantum circuits incrementally by appending quantum gates selected with Bayesian information criterion as circuit selection metric and Bayesian optimization of the parameters of the locally optimal quantum circuits identified. The goal is to build quantum kernels for SVM that can solve classification problems with as little training data as possible. The performance of the resulting quantum models for the classification problems considered here significantly exceeds that of optimized classical models with conventional kernels.
