| Autor: |
Thomas Fösel, Petru Tighineanu, Talitha Weiss, Florian Marquardt |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
Physical Review X, Vol 8, Iss 3, p 031084 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2160-3308 |
| DOI: |
10.1103/PhysRevX.8.031084 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
