Journal article icon

Journal article

Deep reinforcement learning for efficient measurement of quantum devices

Abstract:

Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1038/s41534-021-00434-x

Authors


Expand authors...
Publisher:
Springer Nature Publisher's website
Journal:
npj Quantum Information Journal website
Volume:
7
Issue:
1
Article number:
100
Publication date:
2021-06-18
Acceptance date:
2021-05-24
DOI:
EISSN:
2056-6387
Language:
English
Keywords:
Pubs id:
1183423
Local pid:
pubs:1183423
Deposit date:
2021-07-08

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP