Communication-efficient Quantum Algorithm for Distributed Machine Learning

• URL: http://arxiv.org/abs/2209.04888v1
• Date: Sun, 11 Sep 2022 15:03:58 GMT
• Title: Communication-efficient Quantum Algorithm for Distributed Machine Learning
• Authors: Hao Tang, Boning Li, Guoqing Wang, Haowei Xu, Changhao Li, Ariel Barr, Paola Cappellaro, Ju Li
• Abstract summary: Our quantum algorithm finds the model parameters with a communication complexity of $O(fraclog_2(N)epsilon)$, where $N$ is the number of data points and $epsilon$ is the bound on parameter errors. 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.
• Score: 14.546892420890943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $O(\frac{\log_2(N)}{\epsilon})$, where $N$ is the number of data points and $\epsilon$ 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.

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