标题： 基于量子机器学习的进行性核上性麻痹网络分类应用 显示英文标题

标题： Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification

摘要： 机器学习和量子计算正在被逐步探索，以揭示可能的计算方法来解决以前无法解决的问题。 机器学习的经典方法在模式识别中无处不在，支持向量机（SVM）是网络分类的一种显著技术。 然而，当输入特征空间变得庞大时，这些分类实例的成功解决存在局限性，而所谓的核函数的连续评估在计算上变得极其昂贵。 使用主成分分析（PCA）大大降低了特征空间的维度，从而实现了监督学习的计算加速：创建分类器。 此外，将基于量子的学习应用于PCA减少的输入特征空间可能会以更少的参数提供指数级的加速。 当前的学习模型在真实的临床应用中进行了评估：进行进行性核上性麻痹（PSP）疾病的诊断。 结果表明，量子机器学习已经取得了显著的进步，并优于经典框架。 优化的变分量子分类器对PSP数据集的分类准确率为86%，相比之下传统的SVM。 另一种技术，量子核估计器，在量子机器上近似核函数并优化经典SVM。 特别是，我们已经证明了该模型在IBM量子平台的量子模拟器和真实芯片上的成功应用。 显示英文摘要

摘要： Machine learning and quantum computing are being progressively explored to shed light on possible computational approaches to deal with hitherto unsolvable problems. Classical methods for machine learning are ubiquitous in pattern recognition, with support vector machines (SVMs) being a prominent technique for network classification. However, there are limitations to the successful resolution of such classification instances when the input feature space becomes large, and the successive evaluation of so-called kernel functions becomes computationally exorbitant. The use of principal component analysis (PCA) substantially minimizes the dimensionality of feature space thereby enabling computational speed-ups of supervised learning: the creation of a classifier. Further, the application of quantum-based learning to the PCA reduced input feature space might offer an exponential speedup with fewer parameters. The present learning model is evaluated on a real clinical application: the diagnosis of Progressive Supranuclear Palsy (PSP) disorder. The results suggest that quantum machine learning has led to noticeable advancement and outperforms classical frameworks. The optimized variational quantum classifier classifies the PSP dataset with 86% accuracy as compared to conventional SVM. The other technique, a quantum kernel estimator, approximates the kernel function on the quantum machine and optimizes a classical SVM. In particular, we have demonstrated the successful application of the present model on both a quantum simulator and real chips of the IBM quantum platform.