QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

TL;DR

QuantumFed introduces a federated learning framework for collaborative training of quantum neural networks across multiple quantum machines, addressing data privacy and computational challenges in quantum machine learning.

Contribution

This paper proposes QuantumFed, the first quantum federated learning framework enabling multiple quantum nodes to collaboratively train models without data sharing.

Findings

Demonstrates feasibility of quantum federated learning

Shows robustness of the QuantumFed framework

Validates effectiveness through experimental results

Abstract

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.