Quantum Federated Learning

Quantum Federated Learning (QFL) combines quantum machine learning with federated learning to enable collaborative training of quantum models across distributed devices while preserving data privacy. Current research focuses on developing efficient algorithms, such as variations of quantum natural gradient descent, and adapting quantum neural network architectures (including LSTMs) for this distributed setting, often addressing challenges like non-independent and identically distributed (non-IID) data and noisy communication channels. This approach holds significant promise for enhancing the security and efficiency of machine learning in sensitive applications like finance and healthcare, while also advancing the understanding of quantum algorithms in distributed environments.