Deep reinforcement learning for robust quantum optimization

Understand

Machine learning techniques based on artificial neural networks have been successfully applied to solve many problems in science.

• One of the most interesting domains of machine learning, reinforcement learning, has natural applicability for optimization problems in physics.
• In this work we use deep reinforcement learning and Chopped Random Basis optimization, to solve an optimization problem based on the insertion of an off-center barrier in a quantum Szilard engine.
• We show that using designed protocols for the time dependence of the barrier strength, we can achieve an equal splitting of the wave function (1/2 probability to find the particle on either side of the barrier) even for an asymmetric Szilard engine in such a way that no information is lost when measuring which side the particle is found.