Explainable Classification Techniques for Quantum Dot Device Measurements

Published

March 12, 2024

Author(s)

Daniel Schug, Tyler Kovach, Jared Benson, Mark Eriksson, Justyna Zwolak

Abstract

In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two-dimensional data, is now widespread across a large number of fields, including quantum information science, which we consider here. While traditional image features are widely utilized in such cases, their use is rapidly being supplanted by Neural Network-based techniques that often sacrifice explainability in exchange for high accuracy. To ameliorate this trade-off, we propose a synthetic data-based technique that results in explainable features. We show, using Explainable Boosting Machines (EBMs), that this method offers superior explainability without sacrificing accuracy. Specifically, we show that there is a meaningful benefit to this technique in the context of quantum dot tuning, where human intervention is necessary at the current stage of development.

Proceedings Title

Proceedings of the Explainable machine learning for sciences workshop, AAAI 2024

Conference Dates

February 26, 2024

Conference Location

Vancouver, CA

Pub Type

Conferences

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Keywords

explainable machine learning, classification, quantum dot devices, autonomous control

Citation

Schug, D. , Kovach, T. , Benson, J. , Eriksson, M. and Zwolak, J. (2024), Explainable Classification Techniques for Quantum Dot Device Measurements, Proceedings of the Explainable machine learning for sciences workshop, AAAI 2024, Vancouver, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957562 (Accessed October 10, 2025)