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.
Reinforcement Learning with Neural Networks for Quantum Feedback
Thomas Fösel, Petru Tighineanu, Talitha Weiss, Florian Marquardt
Physical Review X 8(3) (2018)
Biography: Florian Marquart is a theoretical physicist working at the intersection between nanophysics and quantum optics, and especially in the new field of cavity optomechanics. Cavity optomechanics deals with the interaction between light and mechanical motion.
Since August 2016, he is the director at the Max Planck Institute for the Science of Light in Erlangen, Germany. He also holds a part-time position at the University of Erlangen.