UMR 5672

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Classical neural networks for quantum physics

Florian Marquardt (Max Planck Institute for the Science of Light, Erlangen)
When Mar 23, 2020
from 11:00 AM to 12:00 PM
Where Amphi. Schrödinger
Attendees Florian Marquardt
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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)

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