Intelligent tuning of quantum dot devices
Confining electrons in arrays of semiconductor nanostructures, called quantum dots (QDs), is a promising approach to quantum computing. Due to the ease of control of the relevant parameters, fast measurement of the spin and charge states, relatively long decoherence times, and their potential for scalability, QDs are gaining popularity as building blocks for solid-state quantum devices. However, the relevant parameter space scales exponentially with QD number (dimensionality), making heuristic control unfeasible. In semiconductor quantum computing, devices now have tens of individual electrostatic and dynamical gate voltages that must be carefully set to isolate the system to the single electron regime and to realize good qubit performance. We work on automating all phases of the QD device calibration and control.
Smart control of cold atom systems
Coming soon.
Scientific databases
Coming soon.
Network analysis
We employ social network analysis to understand factors that affect student persistence in introductory physics courses. Our work resulted in a number of surprising findings. For instance, we found that social integration within a peer community is more important than grades in predicting persistence for certain cohorts of students. This work has been highlighted in Science, Nature Physics, and as the Editors’ Suggestion in Physical Review.
Likert-style surveys are widely used research instruments to assess respondents’ preferences, beliefs, or experiences. We proposed a complex network analysis approach to identify and study the interconnectedness of items in Likert-style surveys, providing an analysis tool that complements the traditionally employed methods such as PCA or EFA.