研究者詳細

顔写真

ザイルースキ　マイケル　ライアン
Zielewski Michael Ryan
Zielewski Michael Ryan
所属
未踏スケールデータアナリティクスセンター　ソーシャルインテグレーション研究部門
職名
助教
学位

  • 博士（情報科学）（東北大学）

  • 修士（情報科学）（東北大学）

e-Rad 研究者番号
51003876

論文 5

  1. Automatic detection of single-electron regime and virtual gate definition in quantum dots using U-Net and clustering

    Yui Muto, Michael R. Zielewski, Motoya Shinozaki, Kosuke Noro, Tomohiro Otsuka

    Scientific Reports　2026年2月14日

    DOI： 10.1038/s41598-026-38889-7 　

    ISSN：2045-2322

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    <jats:title>Abstract</jats:title> <jats:p>To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices.</jats:p>

  2. Chimera-VDB: Mixed-Precision Vector Database with HNSW Index for RAG-LLM

    Naoshi Yamane, Michael Ryan Zielewski, Takaki Nakamura, Takuo Suganuma

    Proceedings of the 16th ACM SIGOPS Asia-Pacific Workshop on Systems　61-67　2025年10月11日

    出版者・発行元： ACM

    DOI： 10.1145/3725783.3764411 　

  3. Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

    Michael Zielewski, Keichi Takahashi, Yoichi Shimomura, Hiroyuki Takizawa

    IEEE Access　11　104285-104294　2023年

    DOI： 10.1109/ACCESS.2023.3317698 　

    ISSN：2169-3536

  4. A Method for Reducing Time-to-Solution in Quantum Annealing Through Pausing

    Michael Ryan Zielewski, Hiroyuki Takizawa

    International Conference on High Performance Computing in Asia-Pacific Region　7　137-145　2022年1月7日

    出版者・発行元： ACM

    DOI： 10.1145/3492805.3492815 　

  5. Improving Quantum Annealing Performance on Embedded Problems

    Michael Zielewski

    Supercomputing Frontiers and Innovations　7　(4)　32-48　2020年12月

    DOI： 10.14529/jsfi200403 　

    ISSN：2313-8734

MISC 1

  1. Procedural Content Generation via Generative Artificial Intelligence

    Xinyu MAO, Wanli YU, Yuya OKAWARA, Xueying ZHAN, Kazunori D. YAMADA, Michael R. ZIELEWSKI

    Interdisciplinary Information Sciences　abs/2407.09013　2026年

    DOI： 10.4036/iis.2026.r.01 　

    ISSN： 1340-9050 1347-6157

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    The attempt to utilize machine learning in PCG has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While generative AI is effective for PCG, one significant issues it faces is that building high-performance generative AI requires vast amounts of training data. Because content generally highly customized, domain-specific training data is scarce, and straightforward approaches to generative AI models may not work well. For PCG research to advance further, issues related to limited training data must be overcome. Thus, we also give special consideration to research that addresses the challenges posed by limited training data.

共同研究・競争的資金等の研究課題 1

  1. A Study on the Efficient Use of Quantum Annealers with Search Trees

    ZIELEWSKI MICHAEL・RYAN

    2024年7月31日 ～ 2025年3月31日