LossAgg-QFL: Communication-Efficient Quantum Federated Learning via Loss Aggregation

A novel method that aggregates loss values instead of gradients in quantum federated learning, reducing communication overhead and quantum measurements by orders of magnitude while maintaining strong convergence under non-IID data distribution.

QSyn: A Developer-Friendly Quantum Circuit Synthesis Framework for NISQ Era and Beyond

Developed a C++-based quantum circuit compilation framework that provides a unified environment for prototyping and evaluating quantum circuit synthesis algorithms, with robust developer tools including CI/CD and regression testing.