Information Technology Laboratory, Applied and Computational Mathematics Division
Machine Learning (ML) and artificial intelligence (AI) are beginning to broadly impact physics: from probing the evolution of galaxies to calculating quantum wave functions to discovering new states of matter. This postdoctoral research opportunity centers on developing autonomous ML-driven systems for measurement, calibration, and control of quantum systems and quantum computing platforms.
Working closely with scientists in the Physics Measurement Laboratory at NIST, as well as external collaborators, a successful candidate will extend our recently developed ML-driven autonomous systems for state assessment, calibration, and control of quantum information science systems. This work will specifically focus on combining ML algorithms for in situ classification of laboratory quantum systems, in real-time, during the operating experiment. The candidate will develop custom optimization algorithms, leading to an automated control protocol. The proposed protocols will be implemented and validated experimentally. 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. Phys. Rev. Applied 13, 034075 (2020).
 J. P. Zwolak, T. McJunkin et al. Ray-based framework for state identification in quantum dot devices. PRX Quantum (accepted) arXiv:2102.11784 (2021).
 S. Guo et al. Machine-learning enhanced dark soliton detection in Bose-Einstein condensates. Mach. Learn.: Sci. and Tech. arXiv:2101.05404 (2021).
machine learning; artificial intelligence; autonomous control; quantum dots; cold atoms; optimization;