Using Machine Learning to Automate the Tuning of Electrostatically Defined Quantum DotsJanuary 1, 2019
HRL Laboratories LLC Computer Science/Physics, 2018-19
Liaison(s): Seán Meenehan ’08, Emily Pritchett
Advisor(s): Peter Saeta
Students(s): Corbin Bethurem (PM-F), Evan Hubinger, John Jeang, Vivian Phun (PM-S)
HRL Laboratories wants to use electrostatically defined quantum dots in order to build qubits for quantum computers. Specifically, HRL Laboratories wants to use a three-dot system with a configuration of one electron per dot. However, the process of tuning up the dots is labor-intensive and extremely slow to do by hand. The goal is to automate the process via machine learning techniques; we use deep reinforcement learning as our machine learning technique.