Automation of quantum dot measurement analysis via explainable machine learning

2024-02-21

Machine Learning: Science and Technology

AI Summary

In automated tuning of quantum dot (QD) devices, triangle plot image analysis suffers from poor interpretability and limited guidance for parameter adjustment. Method: This paper proposes a synthetic triangular mathematical modeling-based feature vectorization approach—replacing black-box CNNs or Gabor transforms—to jointly optimize accuracy and physical interpretability in QD image analysis. The method integrates controllable geometric modeling with explainable boosting machines (EBMs), mapping triangle plots to voltage-parameter-space features endowed with explicit physical meaning and generating device-level tuning recommendations. Results: Evaluated on real QD triangle plot data, the method achieves >95% classification accuracy—comparable to state-of-the-art CNNs—while providing actionable gate-voltage adjustment directions. It significantly enhances transparency, reliability, and automation efficiency in QD device tuning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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 [1]. 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 quantum dot measurements, paving the way for further advances in automated and transparent QD device tuning.

Problem

• Quantum Dot Devices
• Automated Analysis
• Convolutional Neural Networks (CNN)

Innovation

• Interpretable Enhanced Machine Learning
• Quantum Dot Devices
• Triangular Diagram Analysis