Automated Tuning of Electrostatically Defined Quantum Dots
The team designed an algorithm that loads electrons onto silicon heterostructure quantum dots to initialize a qubit. The algorithm relies on an internal electrostatics model of the dot system to navigate through the space of voltages controlling the potential energy landscape. It also uses a modified second model to simulate real experimental feedback during development and testing, since the team did not have access to the laboratory setup. The algorithm queries the internal model to determine a path through voltage space that results in a specific charge configuration for the quantum dot system.
Advisor(s): Gregory A. Lyzenga.
Team: Brynn Elise Arborico ’17, Amy Frances Brown ’17, Max James Byers ’17, and Kathleen Elizabeth Kohl ’17.