Encoding classical inputs into quantum states is considered as a quantum feature map to map the classical data into the quantum Hilbert space. This feature map paves opportunities to merge the advantages of quantum mechanics into machine learning algorithms to perform on the near-term intermediate-scale quantum computers. While the quantum feature map has demonstrated its capability when combining with linear classification models in some specific applications, its expressive power from the theoretical perspective remains unknown. We prove that the quantum feature map is a universal approximator of continuous functions under its typical settings in many practical applications. We further study the capability of the quantum feature map in the classification of disjoint regions. Our work enables a theoretical analysis of the feasibility of quantum-enhanced machine learning algorithms. In light of this, one can utilize knowledge to design a quantum machine learning model with more powerful expressivity.
翻译:将古典输入编码成量子状态被视为将古典数据映射到量子Hilbert空间的量子特征地图。 这个特征地图为将量子力学的优势整合为机器学习算法以在近期中期中型量子计算机上运行提供了机会。 虽然量子特征地图在与某些具体应用的线性分类模型相结合时显示了其能力,但从理论角度看,其表达力仍然未知。 我们证明量子特征地图是其典型环境中在许多实际应用中连续功能的通用近似体。 我们进一步研究了脱节区域分类中的量子特征地图的能力。 我们的工作使得能够对量子增强机学习算法的可行性进行理论分析。 有鉴于此,我们可以利用知识设计一个具有更强大表达性的量子机器学习模型。