Reinforcement Learning with Neural Networks for Quantum Feedback
Foesel T, Tighineanu P, Weiss T, Marquardt F (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 8
Article Number: 031084
Journal Issue: 3
DOI: 10.1103/PhysRevX.8.031084
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Foesel, T., Tighineanu, P., Weiss, T., & Marquardt, F. (2018). Reinforcement Learning with Neural Networks for Quantum Feedback. Physical Review X, 8(3). https://doi.org/10.1103/PhysRevX.8.031084
MLA:
Foesel, Thomas, et al. "Reinforcement Learning with Neural Networks for Quantum Feedback." Physical Review X 8.3 (2018).
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