College of Science

Seminar: Justyna P. Zwolak [QuICS and NIST]

Machine Learning for Experimental Quantum-Dot Control

Mar 8, 2018 11:00 AM to Jan 8, 2015 12:00 PM at Building 8 room 241

Arrays of gate-defined quantum dots provide a promising platform for the realization of quantum computers. With experimental efforts moving towards such arrays, a new control challenge presents itself - determination of appropriate regions in the gate voltage space to allow efficient control and manipulation of the electrons. In the past, this challenge has been tackled with heuristic approaches. Machine learning tools have emerged as a practical toolkit for automated heuristics. I will describe our efforts to enable machine learning based auto-tuning of quantum dot arrays. A prerequisite is the availability of a training data-set that can qualitatively model the observed current and charge-sensor outputs. We estimate capacitance and tunneling models of arrays under the Thomas-Fermi and WKB approximations. We then describe the learning problems on these datasets and outline an architecture for auto-tuning.

10:50 am Refreshments

11:00 am Seminar

Building 8, Room 241