论文标题
用于分布式机器学习的沟通高效量子算法
Communication-efficient Quantum Algorithm for Distributed Machine Learning
论文作者
论文摘要
远程检测和越来越多的培训数据的需求不断增长，这使得在通信约束下的分布式机器学习成为关键问题。这项工作提供了一种沟通效率的量子算法，该算法解决了两个传统的机器学习问题，即最小二乘拟合和软马克斯回归问题，在数据集分布在两个方案的情况下。我们的量子算法找到具有$ o（\ frac {\ log_2（n）}ε）$的通信复杂性的模型参数，其中$ n $是数据点的数量，$ε$是对参数错误的限制。与实现相同输出任务的经典算法和其他量子算法相比，我们的算法在数据量的缩放量表中提供了通信优势。我们的算法的构建基础，分布式内部产品和锤击距离的量子加速估计，可以进一步应用于分布式机器学习中的各种任务以加速通信。
The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problem, in the scenario where the data set is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of $O(\frac{\log_2(N)}ε)$, where $N$ is the number of data points and $ε$ is the bound on parameter errors. Compared to classical algorithms and other quantum algorithms that achieve the same output task, our algorithm provides a communication advantage in the scaling with the data volume. The building block of our algorithm, the quantum-accelerated estimation of distributed inner product and Hamming distance, could be further applied to various tasks in distributed machine learning to accelerate communication.
