Quantum machine learning faces applicability challenges as quantum computers are needed for both training and evaluation of trained models. This study explores models that can be quantumly trained but classically evaluated, highlighting their limits compared to fully quantum models and their advantages over classical ones.
- Sofiene Jerbi
- Casper Gyurik
- Vedran Dunjko