KEIO RESEARCHERS INFORMATION SYSTEM

Other Affiliation 【 Display / hide 】

Other Affiliation 【 Display / hide 】

• サスティナブル量子AI研究センター, Chair

• Keio University Human Biology Microbiome Quantum Research Center (WPI-Bio2Q), Core Director

• Keio University Quantum Computing Center, KQCC Researcher

Career 【 Display / hide 】

Career 【 Display / hide 】

• 2008.04

  -

  2010.03

  The University of Tokyo, Institute of Solid State Physics, Postdoctoral fellow

• 2010.04

  -

  2011.03

  Kindai University, Quantum Computing Center, 博士研究員

• 2011.04

  -

  2014.03

  The University of Tokyo, Department of Chemistry, Research Fellowship for Young Scientists, Japan Society for the Promotion of Science

• 2014.04

  -

  2015.03

  Kyoto University, Yukawa Institute for Theoretical Physics, Postdoctoral Fellow (Yukawa Fellow)

• 2014.10

  -

  2015.01

  Kyoto University, Faculty of Integrated Human Studies, Part-time Lecturer

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Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 1999.04

  -

  2003.03

  Tokyo Institute of Technology, School of Science, Department of Physics

  University, Graduated

• 2003.04

  -

  2005.03

  The University of Tokyo, School of Science, Department of Physics

  Graduate School, Completed, Master's course

• 2005.04

  -

  2008.03

  The University of Tokyo, School of Science, Department of Physics

  Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• Ph. D, The University of Tokyo, Coursework, 2008.03

  Slow Dynamics in Frustrated Magnetic Systems

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Natural Science / Mathematical physics and fundamental theory of condensed matter physics

Research Keywords 【 Display / hide 】

Research Keywords 【 Display / hide 】

• 量子アニーリング

• イジングマシン

• 物性理論

• 統計力学

• 計算物理学

Research Themes 【 Display / hide 】

Research Themes 【 Display / hide 】

• Quantum annealing, Ising machine, 

  2006

  -

  Present

  　View Summary

  量子アニーリング等イジングマシンのハードウェア開発やソフトウェア・内部アルゴリズム開発につながる基礎研究や、量子アニーリング等イジングマシンの有効なアプリケーションを探る応用研究を、多くの企業や大学、研究所の方々と緊密に連携しながら行っております。

Proposed Theme of Joint Research 【 Display / hide 】

Proposed Theme of Joint Research 【 Display / hide 】

• 量子アニーリング等イジングマシンの有効なアプリケーション探索

  Interested in joint research with industry (including private organizations, etc.),  Desired form： Technical Consultation, Funded Research, Cooperative Research

• 量子アニーリング等イジングマシンのソフトウェア開発につながる基礎研究

  Interested in joint research with industry (including private organizations, etc.),  Desired form： Technical Consultation, Funded Research, Cooperative Research

• 量子アニーリング等イジングマシンのハードウェア開発につながる基礎研究

  Interested in joint research with industry (including private organizations, etc.),  Desired form： Technical Consultation, Funded Research, Cooperative Research

Books 【 Display / hide 】

Books 【 Display / hide 】

• 量子アニーリングの物理

  田中宗,田村亮, 森北出版, 2023.01

  Original author： Shu Tanaka, Ryo Tamura, and Bikas K. Chakrabarti

• 弱点克服　大学生の熱力学

  田中宗,轟木義一,田村亮,白井達彦,所裕子, 東京図書, 2021.11

• Quantum Spin Glasses, Annealing and Computation

  Shu Tanaka, Ryo Tamura, and Bikas K. Chakrabarti, Cambridge University Press, 2017.05,  Page： 418

• Kinki University Series on Quantum Computing Series “Lectures on Quantum Computing, Thermodynamics and Statistical Physics”

  Shu Tanaka and Ryo Tamura, World Scientific, 2012.12

  Scope: Quantum Annealing from the Viewpoint of Statistical Physics, Condensed Matter Physics, and Computational Physics,  Contact page： 3-59

• Lecture Note in Physics “Quantum Quenching, Annealing, and Computation”

  Shu Tanaka, Masaki Hirano, and Seiji Miyashita, Springer, 2010.05

  Scope: Roles of Quantum Fluctuation in Frustrated Systems -- Order by Disorder and Reentrant Phase Transition --,  Contact page： 215-234

Papers 【 Display / hide 】

Papers 【 Display / hide 】

• Quantitative Analysis of the Effectiveness of Mid-anneal Measurement in Quantum Annealing

  Takahashi K., Tanaka S.

  Journal of the Physical Society of Japan 95 ( 3 )  2026.03

  ISSN  00319015

  　View Summary

  Quantum annealing is a promising metaheuristic for solving constrained combinatorial optimization problems. However, coefficient tuning difficulties and hardware noise often prevent optimal solutions from being properly encoded as the ground states of the problem Hamiltonian. This study investigates mid-anneal measurement as a mitigation approach for such situations, analyzing its effectiveness and underlying physical mechanisms. We introduce a quantitative metric to evaluate the effectiveness of mid-anneal measurement and apply it to the graph bipartitioning problem and the quadratic knapsack problem. Our findings reveal that mid-anneal measurement is most effective when the energy difference between desired solutions and ground states is small, with effectiveness strongly governed by the energy structure. Moreover, analysis of fully-connected Ising models demonstrates that the effectiveness of mid-anneal measurement persists with increasing system size, indicating its scalability and practical applicability to large-scale quantum annealing.

  • Access to Document (DOI)

• An Ising machine formulation for design updates in topology optimization of flow channels

  Suzuki Y., Aoki S., Key F., Endo K., Matsuda Y., Tanaka S., Behr M., Muramatsu M.

  Engineering with Computers 42 ( 1 )  2026.02

  ISSN  01770667

  　View Summary

  Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method from the literature using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization.

  • Access to Document (DOI)

• SWIFT-FMQA: Enhancing Factorization Machine With Quadratic-Optimization Annealing via Sliding Window

  Nakano M., Seki Y., Kikuchi S., Tanaka S.

  IEEE Access 14   10977 - 10990 2026

  　View Summary

  Derivative-free (DF) optimization problems aim to identify an input that maximizes or minimizes the output of an objective function whose input-output relationship is unknown. Factorization machine with quadratic-optimization annealing (FMQA) is a promising approach to this task, employing a factorization machine (FM) as a surrogate model to iteratively guide the solution search via an Ising machine. Although FMQA has demonstrated strong optimization performance across various applications, its performance often stagnates as the number of optimization iterations increases. One contributing factor to this stagnation is the growing number of data points in the dataset used to train FM. As more data are accumulated, the contribution of newly added data points tends to become diluted within the entire dataset. Based on this observation, we hypothesize that such dilution reduces the impact of new data on improving the prediction accuracy of FM. To address this issue, we propose a novel method named sliding window for iterative factorization training combined with FMQA (SWIFT-FMQA). This method improves upon FMQA by utilizing a sliding-window strategy to sequentially construct a dataset that retains at most a specified number of the most recently added data points. SWIFT-FMQA is designed to enhance the influence of newly added data points on the surrogate model. Numerical experiments demonstrate that SWIFT-FMQA obtains lower-cost solutions with fewer objective function evaluations compared to FMQA.

  • Access to Document (DOI)

• Impact of Fixing Spins in a Quantum Annealer with Energy Rescaling

  Hattori T., Irie H., Kadowaki T., Tanaka S.

  Journal of the Physical Society of Japan 94 ( 7 )  2025.07

  ISSN  00319015

  　View Summary

  Quantum annealing is a promising algorithm for solving combinatorial optimization problems. However, various hardware restrictions significantly impede its efficient performance. Size-reduction methods provide an effective approach to addressing large-scale problems but often introduce additional difficulties. A notable hardware restriction is that quantum annealing can handle only a limited number of decision variables, compared to the size of the problem. Moreover, when employing size-reduction methods, the interactions and local magnetic fields in the Ising model — used to represent the combinatorial optimization problem — can become excessively large, making them difficult to implement on hardware. Although prior studies suggest that energy rescaling impacts the performance of quantum annealing, its interplay with size-reduction methods remains unexplored. This study examines the relationship between fixing spins, a promising size-reduction method, and the effects of energy rescaling. Numerical simulations and experiments conducted on a quantum annealer demonstrate that the fixing spins method enhances quantum annealing performance while preserving the spin-chain embedding for a homogeneous, fully connected ferromagnetic Ising model.

  • Access to Document (DOI)

• Machine Learning Supported Annealing for Prediction of Grand Canonical Crystal Structures

  Couzinié Y., Seki Y., Nishiya Y., Nishi H., Kosugi T., Tanaka S., Matsushita Y.I.

  Journal of the Physical Society of Japan 94 ( 4 )  2025.04

  ISSN  00319015

  　View Summary

  This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with annealing machines to find samples to a black-box function that minimize a given loss. The CSP involves determining the optimal arrangement of atoms in a material based on its chemical composition, a critical challenge in materials science. We explore FMQA’s ability to efficiently sample optimal crystal configurations by setting the loss function to the energy of the crystal configuration as given by a predefined interatomic potential. Further, we investigate how well the energies of the various metastable configurations, or local minima of the potential, are learned by the algorithm. Our investigation reveals FMQA’s potential in quick ground state sampling and in recovering relational order between local minima.

  • Access to Document (DOI)

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Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

• 環境と出会いに感謝

  Tanaka, Shu

  新版 窮理図解 (慶應義塾大学理工学部)   ( 40 )  2024.09

• Grateful for my environment and the people I met

  Tanaka, Shu

  New Kyurizukai (Faculty of Science and Technology, Keio University)   ( 40 )  2024.09

• High performance computing for materials simulation by quantum-classical hybrid algorithms

  Tanaka, Shu

  科学研究費補助金研究成果報告書 2023

• Theoretical study of quantum computing based on the hybridization of quantum and temperature effect

  Tanaka, Shu

  学事振興資金研究成果実績報告書 (慶應義塾大学)  2020

Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

• イジングマシン技術の研究開発動向

  田中 宗

  技術解説書「拡大する量子コンピューティング　その社会実装ポテンシャル」 (モバイルコンピューティング推進コンソーシアム(MCPC))   2020.03

  Article, review, commentary, editorial, etc. (other), Single Work

• イジングマシンの動作原理と応用探索の最新動向

  田中 宗，松田 佳希

  表面と真空 63   96 - 103 2020

  Article, review, commentary, editorial, etc. (scientific journal), Joint Work

• 量子アニーリングや関連技術のいまと未来：AQC2019 参加報告

  田中 宗，白井 達彦，藤井 啓祐

  日本物理学会誌 75 ( 5 ) 299 - 302 2020

  Article, review, commentary, editorial, etc. (scientific journal), Joint Work

• 量子アニーリングの応用探索

  田中 宗，西村 直樹，棚橋 耕太郎

  数理科学 2019年7月号 673   47 - 53 2019.07

  Article, review, commentary, editorial, etc. (scientific journal), Joint Work

• イジングマシンに関係するソフトウェア開発およびアプリケーション探索動向

  田中 宗

  量子コンピュータ／イジング型コンピュータ研究開発最前線　〜基礎原理・最新技術動向・実用化に向けた企業の取り組み〜 (情報機構)   2019.02

  Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Single Work

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Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

• イジングマシンを用いたアミューズメントパークの経路最適化手法

  武笠 陽介、若泉 朋弥、田中 宗、戸川 望

  [Domestic presentation]  VLSI設計技術研究会, 

  2020.03

  , 

  Oral presentation (general)

• イジング計算機による3次元直方体パッキング問題の解法

  金丸 翔、寺田 晃太朗、川村 一志、田中 宗、富田 憲範、戸川 望

  [Domestic presentation]  VLSI設計技術研究会, 

  2020.03

  , 

  Oral presentation (general)

• 3 次元直方体パッキング問題のQUBOモデルマッピング

  金丸 翔、寺田 晃太朗、川村 一志、田中 宗、富田 憲範、戸川 望

  [Domestic presentation]  2020年電子情報通信学会総合大会, 

  2020.03

  , 

  Oral presentation (general)

• Quantum Annealing Accelerates Materials Discovery

  Shu Tanaka

  [International presentation]  MANA International Symposium 2020 Jointly with ICYS, 

  2020.03

  , 

  Oral presentation (invited, special)

• イジングマシン分野の研究開発の現状と今後　〜ハード・ソフト・アプリケーション・理論〜

  田中 宗、戸川 望

  [Domestic presentation]  2020年電子情報通信学会総合大会　依頼シンポジウムセッション「組合せ最適化専用イジングマシン周辺技術の現状と展望」, 

  2020.03

  , 

  Oral presentation (invited, special)

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

Research Projects of Competitive Funds, etc. 【 Display / hide 】

• 多段階最適化のための量子・古典ハイブリッド基本アルゴリズムの構築と評価

  2023.12

  -

  2028.03

  文部科学省・量子科学技術研究開発機構, 戦略的イノベーション創造プログラム（SIP）, Principal investigator

• 量子・AIハイブリッド技術の活用を加速する共通ライブラリ基盤の研究開発

  2023.06

  -

  2026.03

  経済産業省・国立研究開発法人 新エネルギー・産業技術総合開発機構, NEDO, Principal investigator

• 負性インダクタンスと熱ゆらぎを積極利用した複雑な最適化問題を解く量子アニーリング

  2023.04

  -

  2028.03

  MEXT,JSPS, Grant-in-Aid for Scientific Research, 基盤研究（S), Coinvestigator(s)

• 量子人材を創出するエコシステムづくり

  2023.04

  -

  2026.03

  文部科学省, Q-LEAP, Coinvestigator(s)

• 量子・古典ハイブリッドテストベッド構築のための課題要件調査

  2022.09

  -

  2023.01

  文部科学省・量子科学技術研究開発機構, Coinvestigator(s)

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Awards 【 Display / hide 】

Awards 【 Display / hide 】

• 第９回日本物理学会若手奨励賞（領域11）

  田中 宗, 2015.03, 日本物理学会, 二次元量子多体系におけるエンタングルメントの研究

  Type of Award： Award from Japanese society, conference, symposium, etc.

• 東京大学大学院理学系研究科研究奨励賞（博士）

  田中 宗, 2008.03, 東京大学大学院理学系研究科

  Type of Award： Other

Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• PRESENTATION TECHNIQUE

  2026

• LABORATORY IN SCIENCE

  2026

• GRADUATE RESEARCH ON FUNDAMENTAL SCIENCE AND TECHNOLOGY 1

  2026

• BACHELOR'S THESIS

  2026

• APPLIED PHYSICS AND PHYSICO-INFORMATIC PRACTICAL RESEARCH A

  2026

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Courses Previously Taught 【 Display / hide 】

Courses Previously Taught 【 Display / hide 】

• オムニバス講義

  お茶の水女子大学

  2019.04

  -

  2020.03

  , 

  Autumn Semester, Lecture, Lecturer outside of Keio

• ディジタルシステム設計

  早稲田大学基幹理工学部

  2019.04

  -

  2020.03

  , 

  Autumn Semester, Lecture, Lecturer outside of Keio

• ディジタルシステム設計

  早稲田大学基幹理工学部

  2018.04

  -

  2019.03

  , 

  Autumn Semester, Lecture, Lecturer outside of Keio

• Exercises for Fundamental Physics B IPSE Course

  早稲田大学先進理工学部

  2017.04

  -

  2018.03

  , 

  Autumn Semester, Seminar

• 物理学実験

  芝浦工業大学通信工学科

  2017.04

  -

  2018.03

  , 

  Laboratory work/practical work/exercise, Lecturer outside of Keio

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Social Activities 【 Display / hide 】

Social Activities 【 Display / hide 】

• 平成30年度第7回生徒研究成果合同発表会助言員

  科学技術振興機構スーパーサイエンスハイスクール事業, 平成30年度第7回生徒研究成果合同発表会, 

  , 

  2019.02

• 平成29年度第6回生徒研究成果合同発表会助言員

  科学技術振興機構スーパーサイエンスハイスクール事業, 平成29年度第6回生徒研究成果合同発表会, 

  , 

  2018.02

• 平成28年度第5回生徒研究成果合同発表会助言員

  科学技術振興機構スーパーサイエンスハイスクール事業, 平成28年度第5回生徒研究成果合同発表会, 

  , 

  2017.02

• サイエンスキャッスル2016関東大会口頭講演審査員

  株式会社リバネス, サイエンスキャッスル2016関東大会, 

  , 

  2016.12

Memberships in Academic Societies 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

• IEEE, 

  2024.05

  -

  Present

• 情報処理学会, 

  2020.04

  -

  Present

• 日本物理学会, 

  2003.12

  -

  Present

Committee Experiences 【 Display / hide 】

Committee Experiences 【 Display / hide 】

• 2024.07

  -

  Present

  Adiabatic Quantum Computing Conference, Conference series steering committee

• 2024.04

  -

  Present

  情報処理学会量子ソフトウェア研究会専門委員

• 2023.06

  -

  Present

  量子ICTフォーラム量子コンピューティング技術推進委員会　技術担当理事（業務執行理事）

• 2021.04

  -

  Present

  Journal of the Physical Society of Japan(JPSJ)第77期編集委員

• 2020.12

  -

  2021.06

  Adiabatic Quantum Computing Conference 2021 (AQC2021) local organizer

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