Opportunity at National Institute of Standards and Technology (NIST)
Autonomous Control of Quantum Systems
Information Technology Laboratory, Applied and Computational Mathematics Division
Please note: This Agency only participates in the February and August reviews.
|Zwolak, Justyna P
Machine Learning and AI are having great impacts across a number of fields of physics, from probing the evolution of galaxies to calculating quantum wave functions to discovering new states of matter. This research opportunity revolves around building machine learning-driven autonomous systems for calibration and control of quantum information science systems.
Working closely with scientists in other NIST laboratories, as well as several external collaborators, we are developing machine learning-driven autonomous systems for calibration and control of quantum information science systems. In particular, we are combining machine learning algorithms for in situ classification of quantum experimental systems (i.e., in real-time, during the experiment) with custom optimization algorithms to design an automated control protocol. The proposed protocols are implemented and validated experimentally.
The current applications of interest include, but are not limited to, tunable quantum dots and cold atom systems.
 S. S. Kalantre, J. P. Zwolak et al. Machine learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Inf. 5 (6): 1–10 (2019).
 J.P. Zwolak, T. McJunkin et al. Auto-tuning of double dot devices in situ with machine learning. arXiv:1909.08030 (2019).
machine learning; quantum dots; cold atoms; optimization; autonomous control
Open to U.S. citizens
Open to Postdoctoral applicants