Automation of quantum dot measurement analysis via explainable machine learning
The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the...
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| Format: | Article |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ada087 |
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| author | Daniel Schug Tyler J Kovach M A Wolfe Jared Benson Sanghyeok Park J P Dodson J Corrigan M A Eriksson Justyna P Zwolak |
| author_facet | Daniel Schug Tyler J Kovach M A Wolfe Jared Benson Sanghyeok Park J P Dodson J Corrigan M A Eriksson Justyna P Zwolak |
| author_sort | Daniel Schug |
| collection | DOAJ |
| description | The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called triangle plots , which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is good and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of bad images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform (Schug et al 2024 Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada) pp 1–6). Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of QD measurements, paving the way for further advances in automated and transparent QD device tuning. |
| format | Article |
| id | doaj-art-10f8030b94bd4f90843fbb33042e4eb8 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-10f8030b94bd4f90843fbb33042e4eb82025-01-13T07:03:55ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101500610.1088/2632-2153/ada087Automation of quantum dot measurement analysis via explainable machine learningDaniel Schug0https://orcid.org/0009-0001-3758-501XTyler J Kovach1https://orcid.org/0009-0007-0807-7300M A Wolfe2Jared Benson3https://orcid.org/0009-0009-1673-5259Sanghyeok Park4J P Dodson5https://orcid.org/0000-0003-4265-5024J Corrigan6M A Eriksson7https://orcid.org/0000-0002-3130-9735Justyna P Zwolak8https://orcid.org/0000-0002-2286-3208Department of Chemistry and Biochemistry, University of Maryland , College Park, MD 20742, United States of America; National Institute of Standards and Technology , Gaithersburg, MD 20899, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaDepartment of Physics, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaNational Institute of Standards and Technology , Gaithersburg, MD 20899, United States of America; Joint Center for Quantum Information and Computer Science, University of Maryland , College Park, MD 20742, United States of America; Department of Physics, University of Maryland , College Park, MD 20742, United States of AmericaThe rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called triangle plots , which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is good and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of bad images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform (Schug et al 2024 Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada) pp 1–6). Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of QD measurements, paving the way for further advances in automated and transparent QD device tuning.https://doi.org/10.1088/2632-2153/ada087explainable machine learningexplainable boosting machinessemiconductor quantum dots |
| spellingShingle | Daniel Schug Tyler J Kovach M A Wolfe Jared Benson Sanghyeok Park J P Dodson J Corrigan M A Eriksson Justyna P Zwolak Automation of quantum dot measurement analysis via explainable machine learning Machine Learning: Science and Technology explainable machine learning explainable boosting machines semiconductor quantum dots |
| title | Automation of quantum dot measurement analysis via explainable machine learning |
| title_full | Automation of quantum dot measurement analysis via explainable machine learning |
| title_fullStr | Automation of quantum dot measurement analysis via explainable machine learning |
| title_full_unstemmed | Automation of quantum dot measurement analysis via explainable machine learning |
| title_short | Automation of quantum dot measurement analysis via explainable machine learning |
| title_sort | automation of quantum dot measurement analysis via explainable machine learning |
| topic | explainable machine learning explainable boosting machines semiconductor quantum dots |
| url | https://doi.org/10.1088/2632-2153/ada087 |
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