Reinforcement Learning with Neural Networks for Quantum Feedback
Thomas Fösel, Petru Tighineanu, Talitha Weiss, Florian Marquardt · Physical Review X · 2018
Machine learning with artificial neural networks is revolutionizing science.The most advanced challenges require discovering answers autonomously.In the domain of reinforcement learning, control strategies are improved according to a reward function.The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes such as playing Go, but its benefits for physics are yet to be demonstrated.Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise.These strategies require feedback adapted to measurement outcomes.Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space.To solve this challenge, we develop two ideas: two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system.Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.