Quantum optimal control with quantum computers: A hybrid algorithm featuring machine learning optimization

We develop a hybrid quantum-classical algorithm to solve an optimal population transfer problem for molecule subject laser pulse. The evolution of the molecular wave function under pulse is simulated on quantum computer, while iteratively shaped via machine learning (evolutionary) algorithm. A method encode computer $n$-electrons discussed, circuits accomplishing its simulation are derived and scalability in terms number operations discussed. Performance noisy intermediate-scale devices (IBM Q X2) provided assess current technological gap. Furthermore tested emulator compare performance evolutionary with standard ones. Our results show that such algorithms able outperform optimization downhill simplex provide comparable more advanced (but quantum-computer unfriendly) as Rabitz's or gradient-based optimization.