Effect of data encoding on the expressive power of variational quantum-machine-learning models
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Quantum computers can be used for supervised learning by treating parametrized quantum circuits as models that map data inputs to predictions. While a lot of work has been done to investigate the practical implications of this approach, many important theoretical properties of these models remain unknown. Here, we investigate how …