论文标题
低复杂性量子主成分分析算法
A Low Complexity Quantum Principal Component Analysis Algorithm
论文作者
论文摘要
在本文中，我们提出了低复杂性量子主成分分析（QPCA）算法。与最先进的QPCA相似，它通过提取数据矩阵的主成分而不是数据矩阵的所有组件来降低尺寸，从而可以大大减少所需的测量样本。但是，我们QPCA比最先进的QPCA的主要优点是，它所需的量子门要少得多。另外，由于量子电路的简化，它更准确。我们在IBM量子计算平台上实施了拟议的QPCA，实验结果与我们的期望一致。
In this paper, we propose a low complexity quantum principal component analysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal components of the data matrix, rather than all components of the data matrix, to quantum registers, so that samples of measurement required can be reduced considerably. However, the major advantage of our qPCA over the state-of-the-art qPCA is that it requires much less quantum gates. In addition, it is more accurate due to the simplification of the quantum circuit. We implement the proposed qPCA on the IBM quantum computing platform, and the experimental results are consistent with our expectations.
