Autonomous Control of Quantum Systems

Information Technology Laboratory, Applied and Computational Mathematics Division

NIST only participates in the February and August reviews.

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 and 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 with classical data analysis and control techniques to develop robust in situ (i.e., in real-time, during the operating experiment) assessment of laboratory quantum systems. The candidate will develop custom optimization algorithms that combine ML with domain knowledge to establish fully automated control protocol. The proposed protocols will be implemented and validated experimentally. Current applications of interest include, but are not limited to, semiconductor quantum dots and cold atom systems.

[1] J. P. Zwolak et al. Auto-tuning of double dot devices in situ with machine learning. Phys. Rev. Applied 13, 034075 (2020).

[3] J. Ziegler et al. Toward Robust Autotuning of Noisy Quantum dot Devices. Phys. Rev. Applied 17, 024069 (2022).

[4]S. Guo et al. Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons. Phys. Rev. Research 4, 023163 (2022).

machine learning; artificial intelligence; autonomous control; optimization; quantum dots; cold atoms;

Citizenship:  Open to U.S. citizens