Abstract

As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a “tunnel coupling lever arm” to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the gigahertz values that are compatible with exchange gates.

Highlights

  • In this letter, we outline automated tuning procedures that are made possible by automatically fitting the interdot charge transition region of charge stability diagrams

  • We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention

  • We use an image analysis toolbox developed at Sandia National Laboratories to accurately analyze charge stability diagrams acquired from a triple quantum dot (TQD) unit cell of a 9-dot linear array.[13,21]

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Summary

Introduction

We outline automated tuning procedures that are made possible by automatically fitting the interdot charge transition region of charge stability diagrams. We use image analysis to locate interdot charge transitions and automatically perform measurements of the interdot tunnel coupling.[24] Using simple feedback protocols, we demonstrate simultaneous tune-up of the interdot tunnel couplings in a Si/SiGe TQD.

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