Reinforcement Learning for Quantum Memory
Machine learning with artificial neural networks is revolutionizing science. In the search for optimal control sequences, where the success can only be judged with some time-delay, reinforcement learning is the method of choice. The power of this technique has been highlighted by spectacular recent successes such as playing Go.
We have explored 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. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.