论文标题
用于二进制分类的量子鉴别器
Quantum Discriminator for Binary Classification
论文作者
论文摘要
量子计算机具有在高维空间中相对快速运行的独特能力 - 这是为了使他们比古典计算机具有竞争优势。在这项工作中，我们提出了一种称为量子歧视器的新型量子机学习模型，该模型利用量子计算机在高维空间中运行的能力。使用O（n logN）时间中的量子型混合算法对量子鉴别器进行训练，并在线性时间内在通用量子计算机上进行推断。量子鉴别器将从给定基准提取的二进制特征以及初始化为零状态的预测Qubit并输出预测标签。我们分析了其在虹膜数据集上的性能，并表明量子歧视器可以达到99％的模拟精度。
Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces -- this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in O(N logN) time, and inferencing is performed on a universal quantum computer in linear time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit initialized to the zero state and outputs the predicted label. We analyze its performance on the Iris data set and show that the quantum discriminator can attain 99% accuracy in simulation.
