用于监督分类的量子态鉴别
Quantum State Discrimination for Supervised Classification
摘要：
本文研究了量子信息理论与机器学习之间的联系。特别是，我们展示了如何量子态歧视可以代表一个有用的工具来解决机器学习中的标准分类问题。先前的研究表明，在量子信息理论和量子通信背景下发展的最优量子测量理论可以启发新的二进制分类算法，该算法可以对各种数据集实现更高的推理精度。在这里，我们提出了一个模型的任意多类分类启发的量子态歧视，这是通过编码的数据在希尔伯特空间上的线性算子的空间。虽然我们的算法是量子启发的，它可以在经典硬件上实现，从而允许立即应用。
英文摘要：
In this paper we investigate the connection between quantum information theory and machine learning. In particular, we show how quantum state discrimination can represent a useful tool to address the standard classification problem in machine learning. Previous studies have shown that the optimal quantum measurement theory developed in the context of quantum information theory and quantum communication can inspire a new binary classification algorithm that can achieve higher inference accuracy for various datasets. Here we propose a model for arbitrary multiclass classification inspired by quantum state discrimination, which is enabled by encoding the data in the space of linear operators on a Hilbert space. While our algorithm is quantum-inspired, it can be implemented on classical hardware, thereby permitting immediate applications.
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