Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
Results show that FL enables high-performing, privacy-preserving quantum-classical collaboration without centralizing raw data, and achieves this with substantially fewer trainable parameters than the classical neural networks and random forest alternatives.
C. Cano, Daniel M. Jimenez-Gutierrez, Diego Sal et al.
· 0 citations