论文标题
带基于距离的分类器的量子一级分类
Quantum One-class Classification With a Distance-based Classifier
论文作者
论文摘要
量子计算中技术的进步为在实际量子设备中执行算法带来了可能性。但是，当前量子硬件和可用量子位数量少的现有错误使得使用更少的Qubits和更少操作的解决方案，从而减轻了此类障碍。 Hadamard分类器（HC）是用于模式识别的基于距离的量子机学习模型。我们提出了一种基于HC的新分类器，该分类器由名为Quantum One-One-One-One-One-One Classifier（QOCC）组成，该分类器由一个最小的量子机学习模型组成，其操作和QUBITS较少，因此能够减轻NISQ（嘈杂的中间尺度量子）计算机的错误。实验结果是通过在量子设备上运行提出的分类器来获得的，并表明QOCC比HC具有优势。
The advancement of technology in Quantum Computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of available qubits make it necessary to use solutions that use fewer qubits and fewer operations, mitigating such obstacles. Hadamard Classifier (HC) is a distance-based quantum machine learning model for pattern recognition. We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a quantum device and show that QOCC has advantages over HC.
