Exponential Quantum Communication Advantage in Distributed Learning

As the scale of the datasets and parameterized models used to perform computation over data continues to grow [43, 53], distributing workloads across multiple devices becomes essential for enabling progress. The choice of architecture for large-scale training and inference must not only make the best use of computational and memory resources, but also contend with the fact that communication may become a bottleneck [85]. When using modern optical interconnects, classical computers exchange bits represented by light. This however does not fully utilize the potential of the physical substrate; given suitable computational capabilities and algorithms, the quantum nature of light can be harnessed as a powerful communication resource. Here we show that for a broad class of parameterized models, if quantum bits (qubits) are communicated instead of classical bits, an exponential reduction in the communication required to perform inference and gradientbased training can be achieved. This protocol additionally guarantees improved privacy of both the user data and model parameters through natural features of quantum mechanics, without the need for additional cryptographic or privacy protocols. To our knowledge, this is the first example of generic, exponential quantum advantage on problems that occur naturally in the training and deployment of large machine learning models. These types of communication advantages help scope the future roles and interplay between quantum and classical communication for distributed machine learning.