论文标题
分布式量子机学习
Distributed Quantum Machine Learning
论文作者
论文摘要
量子计算机可以解决特定的复杂任务，该任务尚无合理的经典算法。但是，随着测量结果破坏量子状态，量子计算机也确实提供了数据的固有安全性。使用共享的纠缠状态，多方可以协作并安全地计算量子算法。在本文中，我们提出了一种分布式量子机学习的方法，该方法允许多方安全执行计算，而无需透露其数据。我们将考虑一个分布式加法器和分布式基于距离的分类器。
Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose an approach for distributed quantum machine learning, which allows multiple parties to securely perform computations, without having to reveal their data. We will consider a distributed adder and a distributed distance-based classifier.
