Quantum Kitchen Sinks：一种在近期量子计算机上进行机器学习的算法
Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers
摘要：
有噪声的中等规模量子计算设备是探索近期量子应用能力的令人兴奋的平台。在这样的设备中执行重要的任务需要一种与纠错量子计算机上使用的方法完全不同的方法。一种这样的方法是使用混合算法，其中问题被简化为通常在经典反馈回路中优化的参数化量子线路。在这里，我们描述了一个这样的混合算法的机器学习任务的基础上，称为随机厨房水槽的经典算法。我们的技术被称为量子厨房水槽，使用量子线路将经典输入非线性地转换为可以用于许多机器学习算法的特征。我们证明了这个建议的力量和灵活性，使用它来解决二进制分类问题的合成数据集，以及手写数字从MNIST数据库。使用Rigetti量子虚拟机，我们表明，小量子线路提供了显着的性能提升超过标准的线性经典算法，降低分类错误率从50%到$<0.1\%$，并从$4.1\%$到$1.4\%$在这两个例子中，分别。此外，我们能够使用全尺寸的MNIST图像在Rigetti量子处理单元上运行MNIST分类问题，发现线性基线上的适度性能提升。
英文摘要：
Noisy intermediate-scale quantum computing devices are an exciting platform for the exploration of the power of near-term quantum applications. Performing nontrivial tasks in such devices requires a fundamentally different approach than what would be used on an error-corrected quantum computer. One such approach is to use hybrid algorithms, where problems are reduced to a parameterized quantum circuit that is often optimized in a classical feedback loop. Here we describe one such hybrid algorithm for machine learning tasks by building upon the classical algorithm known as random kitchen sinks. Our technique, called quantum kitchen sinks, uses quantum circuits to nonlinearly transform classical inputs into features that can then be used in a number of machine learning algorithms. We demonstrate the power and flexibility of this proposal by using it to solve binary classification problems for synthetic datasets as well as handwritten digits from the MNIST database. Using the Rigetti quantum virtual machine, we show that small quantum circuits provide significant performance lift over standard linear classical algorithms, reducing classification error rates from 50% to $<0.1\%$, and from $4.1\%$ to $1.4\%$ in these two examples, respectively. Further, we are able to run the MNIST classification problem, using full-sized MNIST images, on a Rigetti quantum processing unit, finding a modest performance lift over the linear baseline.
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