Reinforcement Learning for Quantum Technologies

Explore how reinforcement learning techniques from computer science can be applied to modern quantum devices in this comprehensive lecture by Florian Marquardt, Scientific Director at the Max Planck Institute for the Science of Light. Discover the potential of reinforcement learning in discovering quantum control and feedback strategies for preparing and stabilizing quantum states, as well as performing quantum error correction. Learn about theoretical proposals and groundbreaking experimental implementations, including the first reinforcement learning of real-time quantum feedback in collaboration with ETH's superconducting-qubit team. Drawing from his extensive background in nanophysics and quantum optics, particularly in cavity optomechanics and superconducting circuit quantum electrodynamics, Marquardt demonstrates how machine learning can advance scientific discovery and optimize quantum circuits.