IBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, UK.
Nature. 2019 Mar;567(7747):209-212. doi: 10.1038/s41586-019-0980-2. Epub 2019 Mar 13.
Machine learning and quantum computing are two technologies that each have the potential to alter how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous in pattern recognition, with support vector machines (SVMs) being the best known method for classification problems. However, there are limitations to the successful solution to such classification problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element in the computational speed-ups enabled by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here we propose and experimentally implement two quantum algorithms on a superconducting processor. A key component in both methods is the use of the quantum state space as feature space. The use of a quantum-enhanced feature space that is only efficiently accessible on a quantum computer provides a possible path to quantum advantage. The algorithms solve a problem of supervised learning: the construction of a classifier. One method, the quantum variational classifier, uses a variational quantum circuit to classify the data in a way similar to the method of conventional SVMs. The other method, a quantum kernel estimator, estimates the kernel function on the quantum computer and optimizes a classical SVM. The two methods provide tools for exploring the applications of noisy intermediate-scale quantum computers to machine learning.
机器学习和量子计算是两种技术，它们各自都有潜力改变计算的执行方式，以解决以前无法解决的问题。机器学习的核方法在模式识别中无处不在，支持向量机（SVM）是分类问题中最著名的方法。然而，当特征空间变得很大，核函数的计算变得昂贵时，这种分类问题的成功解决就存在限制。量子算法实现计算加速的核心要素是通过可控的纠缠和干涉来利用指数级大的量子态空间。在这里，我们在超导处理器上提出并实验实现了两种量子算法。这两种方法的一个关键组成部分是使用量子态空间作为特征空间。使用仅在量子计算机上才能有效地访问的量子增强特征空间为量子优势提供了一种可能的途径。这些算法解决了监督学习的一个问题：分类器的构建。一种方法，量子变分分类器，使用变分量子电路以类似于传统 SVM 方法的方式对数据进行分类。另一种方法是量子核估计器，它在量子计算机上估计核函数并优化经典 SVM。这两种方法为探索噪声中等规模量子计算机在机器学习中的应用提供了工具。
