Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks
R. Durrer, Benedikt Kratochwil, Jonne Koski, Andreas Landig, Christian Reichl, W. Wegscheider, Thomas Ihn, Eliška Greplová
Abstract
Semiconductor quantum dots are at the forefront of quantum device technology. One longstanding obstacle to scalability is that multidot systems require a lengthy, complex, experimental tuning process. Here the authors introduce a machine-learning-driven algorithm for automated tuning of quantum dots. By letting the algorithm learn from experimental data, they develop a procedure that uses a small set of measurements as its input, and then automatically tunes the double-dot system to the desired charge state. This constitutes a significant step toward fully automated operation of multidot quantum systems.
Topics & Concepts
Quantum dotQubitComputer scienceCharge (physics)Process (computing)State (computer science)QuantumPhysicsAlgorithmQuantum mechanicsOperating systemQuantum and electron transport phenomenaAdvancements in Semiconductor Devices and Circuit DesignQuantum Computing Algorithms and Architecture