Generalized autonomous optimization for quantum transmitters with deep reinforcement learning

Yuen San Lo; Robert I. Woodward; Taofiq K. Paraiso; Rudra P. K. Poudel; Andrew J. Shields

doi:10.1117/12.3000842

13 March 2024 Generalized autonomous optimization for quantum transmitters with deep reinforcement learning

Yuen San Lo, Robert I. Woodward, Taofiq K. Paraiso, Rudra P. K. Poudel, Andrew J. Shields

Proceedings Volume 12911, Quantum Computing, Communication, and Simulation IV; 1291117 (2024) https://doi.org/10.1117/12.3000842
Event: SPIE Quantum West, 2024, San Francisco, California, United States

Abstract

Precise control of system parameters and extensive optimization play a crucial role in enabling quantum information technologies. As a further challenge, when targeting practical manufacturable systems, the presence of manufacturing variations in components necessitates individual optimization for each system. To address this challenge, we develop a generalisable optimisation framework based on deep reinforcement learning (RL). By applying our method to real-world quantum transmitters based on optical injection locking (OIL), we demonstrate that our RL agent can autonomously identify the optimal operating regions, and generalise its knowledge for new quantum transmitters of the same type. This work presents a new avenue for efficient optimisation of complex systems using modern RL algorithm.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuen San Lo, Robert I. Woodward, Taofiq K. Paraiso, Rudra P. K. Poudel, and Andrew J. Shields "Generalized autonomous optimization for quantum transmitters with deep reinforcement learning", Proc. SPIE 12911, Quantum Computing, Communication, and Simulation IV, 1291117 (13 March 2024); https://doi.org/10.1117/12.3000842

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available