论文标题
在被困的离子量子计算机上最近的质心分类
Nearest Centroid Classification on a Trapped Ion Quantum Computer
论文作者
论文摘要
近年来，Quantum机器学习在理论和实践发展中已经有了相当大的理论和实践发展，并且已成为寻找量子计算机现实世界应用的有希望的领域。为了实现这一目标，我们在这里结合了最先进的算法和量子硬件，以提供量子机学习应用程序的实验证明，并提供可证明其性能和效率的保证。 In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
