Reinforcement Learning with Neural Networks for Quantum Feedback

TL;DR

This paper demonstrates how neural-network-based reinforcement learning can autonomously discover quantum-error-correction strategies, showcasing its potential for advancing quantum computing and physics.

Contribution

It introduces a novel reinforcement learning approach with two-stage learning and a specialized reward to find quantum error correction methods without human guidance.

Findings

Successfully discovered quantum-error-correction strategies

Demonstrated adaptability to different hardware resources

Showed potential of neural reinforcement learning in physics

Abstract

Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. This is the domain of reinforcement learning, where 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, tailored to different hardware resources, is a formidable challenge due to the combinatorially large search space. To solve this, we develop two ideas: two-stage learning with teacher/student networks and a reward quantifying the capability to recover the quantum information stored in a multi-qubit system. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.