论文标题
多核量子分类器
Polyadic Quantum Classifier
论文作者
论文摘要
我们在这里介绍一种监督的量子机学习算法，用于NISQ架构上的多类分类。训练了一个参数量子电路，以输出与输入数据点类相对应的特定位字符串。我们在IBMQ 5 QUITANT量子计算机上进行训练和测试，并且该算法表现出良好的精度 - 与经典的机器学习模型相比，用于虹膜数据集的三元分类和XOR问题的扩展。此外，我们通过模拟评估算法如何用于二进制和第四纪分类。已知的二进制数据集和合成数据集。
We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy --compared to a classical machine learning model-- for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.
