Autonomous bootstrapping of quantum dot devices

Published

January 28, 2025

Author(s)

Anton Zubchenko, Danielle Middlebrooks, Torbjoern Rasmussen, Lara Lausen, Ferdinand Kuemmeth, Anasua Chatterjee, Justyna Zwolak

Abstract

Semiconductor quantum dots (QDs) are a promising platform for multiple different qubit implementations, all of which are voltage controlled by programmable gate electrodes. However, as the QD arrays grow in size and complexity, tuning procedures that can fully autonomously handle the increasing number of control parameters are becoming essential for enabling scalability. We propose a bootstrapping algorithm for initializing a depletion-mode QD device in preparation for subsequent phases of tuning. During bootstrapping, the QD device functionality is validated, all gates are characterized, and the QD charge sensor is made operational. We demonstrate the bootstrapping protocol in conjunction with a coarse-tuning module, showing that the combined algorithm can efficiently and reliably take a cooled-down QD device to a desired global-state configuration in under 8 min with a success rate of 96 %. Finally, by following heuristic approaches to QD device initialization and combining the efficient ray-based measurement with the rapid radio-frequency reflectometry measurements, the proposed algorithm establishes a reference in terms of performance, reliability, and efficiency against which alternative algorithms can be benchmarked.

Citation

Physical Review Applied

Volume

23

Issue

1

Pub Type

Journals

Download Paper

https://doi.org/10.1103/PhysRevApplied.23.014072

Local Download

Keywords

machine learning, quantum dots, autonomous control, bootstrapping

Citation

Zubchenko, A. , Middlebrooks, D. , Rasmussen, T. , Lausen, L. , Kuemmeth, F. , Chatterjee, A. and Zwolak, J. (2025), Autonomous bootstrapping of quantum dot devices, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.23.014072, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958410 (Accessed September 30, 2025)