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 】

• Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment

  Kikuchi S., Tanaka S.

  Scientific Reports 16 ( 1 )  2026.12

  　View Summary

  The RNA inverse folding problem aims to identify nucleotide sequences that preferentially adopt a given target secondary structure. While various heuristic and machine learning-based approaches have been proposed, many require a large number of sequence evaluations, which limits their applicability when experimental validation is costly. We propose a method to solve the problem using a factorization machine with quadratic-optimization annealing (FMQA). FMQA is a discrete black-box optimization method reported to obtain high-quality solutions with a limited number of evaluations. Applying FMQA to the problem requires converting nucleotides into binary variables. However, the influence of integer-to-nucleotide assignments and binary-integer encoding on the performance of FMQA has not been thoroughly investigated, even though such choices determine the structure of the surrogate model and the search landscape, and thus can directly affect solution quality. Therefore, this study aims both to establish a novel FMQA framework for RNA inverse folding and to analyze the effects of these assignments and encoding methods. We evaluated all 24 possible assignments of the four nucleotides to the ordered integers (0-3), in combination with four binary-integer encoding methods. Our results demonstrated that one-hot and domain-wall encodings outperform binary and unary encodings in terms of the normalized ensemble defect value. In domain-wall encoding, nucleotides assigned to the boundary integers (0 and 3) appeared with higher frequency. In the RNA inverse folding problem, assigning guanine and cytosine to these boundary integers promoted their enrichment in stem regions, which led to more thermodynamically stable secondary structures than those obtained with one-hot encoding.

  • Access to Document (DOI)

• Fair Sampling with Temperature-Targeted QAOA Based on Quantum–Classical Correspondence Theory

  Abe T., Tanaka S.

  Journal of the Physical Society of Japan 95 ( 8 )  2026.08

  ISSN  00319015

  　View Summary

  In combinatorial optimization problems with degenerate ground states, fair sampling of degenerate solutions is essential. However, the quantum approximate optimization algorithm (QAOA) with a standard transverse-field mixer induces biases among degenerate states as circuit depth increases. Based on quantum–classical correspondence theory, we propose SBO-QAOA, which employs a temperature-dependent Hamiltonian encoding a Gibbs distribution as its ground state. Numerical simulations show that, unlike standard QAOA, SBO-QAOA yields ground-state probabilities converging to finite-temperature values with uniform distribution among degenerate states. These fairness and temperature-targeting properties are preserved even with only four variational parameters under a linear schedule.

  • Access to Document (DOI)

• Structural Comparison of Error Mitigation Methods for Ising Machines: Penalty-Spin Model versus Stacked Model

  Abe T., Hino K., Tanaka S.

  Journal of the Physical Society of Japan 95 ( 7 )  2026.07

  ISSN  00319015

  　View Summary

  Error-mitigation methods for Ising machines are reexamined not merely as noise-suppression techniques but as a structural design problem of replica-coupled Ising models. Using simulated annealing as a hardware-noise-free testbed, we systematically compare the penalty-spin (PS) model, which couples replicas through a centralized auxiliary layer, with the stacked model, which couples adjacent replicas directly. Numerical experiments on the quadratic assignment problem reveal that the ferromagnetically coupled stacked model stably maintains constraint satisfaction and improves solution quality over a broad parameter range, exhibiting favorable scalability with both the number of replicas and problem size. In contrast, the PS model suffers from cooperation collapse at large parallelism: many-replica averaging in the PS layer washes out sparse solution information, preventing effective inter-replica coordination. These findings demonstrate that the topology of inter-replica couplings decisively influences search robustness, and provide practical guidelines for model selection and parameter tuning in constrained optimization.

  • Access to Document (DOI)

• Black-box optimization using factorization and Ising machines

  Tamura R., Seki Y., Minamoto Y., Kitai K., Matsuda Y., Tanaka S., Tsuda K.

  Applied Physics Reviews 13 ( 2 )  2026.06

  　View Summary

  Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed into a quadratic unconstrained binary optimization model that can be solved using IMs. This makes it possible to optimize the acquisition function in BBO, which is a difficult task using conventional methods without IMs. Consequently, it has the advantage of handling large BBO problems. To be able to perform BBO with the FMQA algorithm immediately, we introduce the FMQA algorithm along with Python packages to run it. In addition, we review examples of applications of the FMQA algorithm in various fields, including physics, chemistry, materials science, and social sciences. These successful examples include binary and integer optimization problems, as well as more general optimization problems involving graphs, networks, and strings, using a binary variational autoencoder. We believe that BBO using the FMQA algorithm will become a key technology in IMs, including quantum annealers.

  • Access to Document (DOI)

• Factorization Machine from a Random Matrix Theory Perspective

  Nakada H., Tanaka S.

  Journal of the Physical Society of Japan 95 ( 6 )  2026.06

  ISSN  00319015

  　View Summary

  Factorization Machine with Quadratic-optimization Annealing (FMQA) is a promising technique for solving black-box optimization problems efficiently. Recently, warm-starting FMQA has been proposed to accelerate an FMQA process by initializing the Factorization Machine (FM) with a pre-computed interaction matrix. However, the mechanism underlying its effectiveness remains unclear. Therefore, in this study, we utilize random matrix theory to investigate the performance of FM initialization. We derive modified results within random matrix theory that describe the statistical properties of min-max normalized eigenvalues in random matrices. Our analysis estimates the residual coupling error that arises during FM initialization via low-rank approximation. Numerical experiments demonstrate that our theoretical predictions for the coupling error exhibit excellent agreement with empirical results. Furthermore, we show that subsequent training effectively reduces the error below the theoretical baseline defined by random matrix theory, confirming that the warm-starting approach provides a robust starting point for optimization. These insights not only reinforce the validity of warm-starting FMQA but also suggest broader applications in efficient model initialization, such as low-rank adaptation for large language models.

  • 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 】

• KDDI Foundation Award 業績賞

  Shu Tanaka, 2025.10, 公益財団法人KDDI財団, 量子アニーリングの基礎、ならびに応用への貢献

  Type of Award： Award from publisher, newspaper, foundation, etc.

• JPSJ Outstanding Referees

  Shu Tanaka, 2025.03, Journal of the Physical Society of Japan

  Type of Award： Honored in official journal of a scientific society, scientific journal

• 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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