Kikuchi, Shuta

写真a

Affiliation

Graduate School of Science and Technology ( Yagami )

Position

Project Assistant Professor (Non-tenured)/Project Research Associate (Non-tenured)/Project Instructor (Non-tenured)

Career 【 Display / hide 】

  • 2019.04
    -
    2022.12

    Lion Corporation, Research and Development Headquarters, Safety Research Science Laboratory, Researcher

  • 2023.01
    -
    2024.03

    Lion Corporation, Research and Development Headquarters Advanced Analytical Science Research Laboratories Microbiological Control Group, Researcher

  • 2024.04
    -
    Present

    Keio University, Graduate School of Science and Technology, Project Assistant Professor

  • 2024.09
    -
    Present

    University of Tsukuba, School of Informatics, College of Information Science, 非常勤講師

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Academic Background 【 Display / hide 】

  • 2013.04
    -
    2017.03

    Waseda University, School of Advanced Science and Engineering, Department of Life Science and Medical Bioscience

  • 2017.04
    -
    2019.03

    Waseda University, Graduate School of Advanced Science and Engineering, Department of Life Science and Medical Bioscience

  • 2021.04
    -
    2024.03

    Keio University, Graduate School of Science and Technology, School of Fundamental Science and Technology Center for Applied Physics and Phusico-Informatioin

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Research Areas 【 Display / hide 】

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

  • Life Science / Applied microbiology

  • Informatics / Life, health and medical informatics

  • Environmental Science/Agriculture Science / Environmental impact assessment

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Research Keywords 【 Display / hide 】

  • Ising machine

  • Simulation

  • Network science

  • Microbiology

  • Infection control science

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Papers 【 Display / hide 】

  • Evaluation of Infection Prevention Measures in Elementary Schools Using an Agent-Based Model

    Shuta Kikuchi, Taisei Mukai, Keisuke Nakajima, Yasuki Kato, Takeshi Takizawa, Junichi Sugiyama, Yasushi Kakizawa, Setsuya Kurahashi

    Advances in Social Simulation    253 - 263 2025.10

    Lead author, Accepted

  • Extending Sample Persistence Variable Reduction for Constrained Combinatorial Optimization Problems

    Shunta Ide, Shuta Kikuchi, Shu Tanaka

    arXiv 2509.19280 2025.09

    　View Summary

    Constrained combinatorial optimization problems (CCOPs) are challenging to solve due to the exponential growth of the solution space. When tackled with Ising machines, constraints are typically enforced by the penalty function method, whose coefficients must be carefully tuned to balance feasibility and objective quality. Variable-reduction techniques such as sample persistence variable reduction (SPVAR) can mitigate hardware limitations of Ising machines, yet their behavior on CCOPs remains insufficiently understood. Building on our prior proposal, we extend and comprehensively evaluate multi-penalty SPVAR (MP-SPVAR), which fixes variables using solution persistence aggregated across multiple penalty coefficients. Experiments on benchmark problems, including the quadratic assignment problem and the quadratic knapsack problem, demonstrate that MP-SPVAR attains higher feasible-solution ratios while matching or improving approximation ratios relative to the conventional SPVAR algorithm. An examination of low-energy states under small penalties clarifies when feasibility degrades and how encoding choices affect the trade-off between solution quality and feasibility. These results position MP-SPVAR as a practical variable-reduction strategy for CCOPs and lay a foundation for systematic penalty tuning, broader problem classes, and integration with quantum-inspired optimization hardware as well as quantum algorithms.

  • Optimization Performance of Factorization Machine with Annealing under Limited Training Data

    Mayumi Nakano, Yuya Seki, Shuta Kikuchi, Shu Tanaka

    arXiv 2507.21024 2025.07

    　View Summary

    Black-box (BB) optimization problems aim to identify an input that minimizes the output of a function (the BB function) whose input-output relationship is unknown. Factorization machine with annealing (FMA) 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 FMA 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. It is hypothesized that as more data points are accumulated, the contribution of newly added data points becomes diluted within the entire dataset, thereby reducing their impact on improving the prediction accuracy of FM. To address this issue, we propose a novel method for sequential dataset construction that retains at most a specified number of the most recently added data points. This strategy is designed to enhance the influence of newly added data points on the surrogate model. Numerical experiments demonstrate that the proposed FMA achieves lower-cost solutions with fewer BB function evaluations compared to the conventional FMA.

  • Effectiveness of Hybrid Optimization Method for Quantum Annealing Machines

    Shuta Kikuchi, Nozomu Togawa, Shu Tanaka

    arXiv 2507.15544 2025.07

    Lead author, Corresponding author

    　View Summary

    To enhance the performance of quantum annealing machines, several methods have been proposed to reduce the number of spins by fixing spin values through preprocessing. We proposed a hybrid optimization method that combines a simulated annealing (SA)-based non-quantum-type Ising machine with a quantum annealing machine. However, its applicability remains unclear. Therefore, we evaluated the performance of the hybrid method on large-size Ising models and analyzed its characteristics. The results indicate that the hybrid method improves upon solutions obtained by the preprocessing SA, even if the Ising models cannot be embedded in the quantum annealing machine. We analyzed the method from three perspectives: preprocessing, spin-fixed sub-Ising model generation method, and the accuracy of the quantum annealing machine. From the viewpoint of the minimum energy gap, we found that solving the sub-Ising model with a quantum annealing machine results in a higher solution accuracy than solving the original Ising model. Additionally, we demonstrated that the number of fixed spins and the accuracy of the quantum annealing machine affect the dependency of the solution accuracy on the sub-Ising model size.

  • Quantification of droplet and contact transmission risks among elementary school students based on network analyses using video-recorded data

    Shuta Kikuchi, Keisuke Nakajima, Yasuki Kato, Takeshi Takizawa, Junichi Sugiyama, Taisei Mukai, Yasushi Kakizawa, Setsuya Kurahashi

    PLOS ONE 20 ( 2 ) e0313364 2025.02

    Lead author, Accepted

    　View Summary

    In elementary schools, immunologically immature students come into close contact with each other and are susceptible to the spread of infectious diseases. To analyze pathogen transmission among students, it is essential to obtain behavioral data. Questionnaires and wearable sensor devices were used for communication behavior and swab sampling was employed for contact behavior. However, these methods have been insufficient in capturing information about the processes and actions of each student that contribute to pathogen transmission. Therefore, in this study, actual behavioral data were collected using video recordings to evaluate droplet and contact transmission in elementary schools. The analysis of communication behavior revealed the diverse nature of interactions among students. By calculating the droplet transmission probabilities based on conversation duration, the risk of droplet transmission was quantified. In the contact behavior, we introduced a novel approach for constructing contact networks based on contact history. According to this method, well-known items, such as students’ desks, doors, and faucets, were predicted to be potential fomite. In addition, students’ shirts and shared items with high contact frequency and high centrality metrics in the network, which were not evaluated in swab sampling surveys, were identified as potential fomites. The reliability of the predictions was demonstrated through micro-simulations. The micro-simulations replicated virus transmission scenarios in which virus-carrying students were present in the actual contact history. The results showed that a significant amount of virus adhered to the items predicted to be fomites. Interestingly, the micro-simulations indicated that most viral copies were transmitted through single items. The analysis of contact history, contact networks, and micro-simulations relies on videorecorded behavioral data, highlighting the importance of this method. This study contributes significantly to the prevention of infectious diseases in elementary schools by providing evidence-based information about transmission pathways and behavior-related risks.

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

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Reviews, Commentaries, etc. 【 Display / hide 】

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Presentations 【 Display / hide 】

  • A dataset construction strategy for factorization machine with annealing to improve optimization performance

    Mayumi Nakano, Yuya Seki, Shuta Kikuchi, Shu Tanaka

    International Network on Quantum Annealing (INQA) Conference 2025, 

    2025.11
    , 

    Poster presentation

  • Overcoming hardware limitations of quantum annealing via spin-variable reduction for linear equality constraints

    Riko Okabe, Shuta Kikuchi, Shu Tanaka

    International Network on Quantum Annealing (INQA) Conference 2025, 

    2025.11
    , 

    Poster presentation

  • Extensions and Evaluation of the Sample Persistence Algorithm for Constrained Combinatorial Optimization Problems

    Shunta Ide, Shuta Kikuchi, Shu Tanaka

    International Network on Quantum Annealing (INQA) Conference 2025, 

    2025.11
    , 

    Oral presentation (general)

  • Extended FMA via Space-Filling Quasi-Random Sequences

    Taiga Hayashi, Yuya Seki, Kotaro Terada, Yosuke Mukasa, Shuta Kikuchi, Shu Tanaka

    International Network on Quantum Annealing (INQA) Conference 2025, 

    2025.11
    , 

    Poster presentation

  • Evaluation of the Effects of Integer Assignment in RNA Inverse Folding Problem Using Factorization Machine with Annealing

    Shuta Kikuchi, Shu Tanaka

    International Network on Quantum Annealing (INQA) Conference 2025, 

    2025.11
    , 

    Oral presentation (general)

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Works 【 Display / hide 】

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Awards 【 Display / hide 】

  • Editage grant

    Shuta Kikuchi, 2024.09, カクタス・コミュニケーションズ株式会社, Exploring Killer Applications of Quantum Annealing Machines for Solving Social Issues

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Other 【 Display / hide 】

  • The Certification Examination for Bioinformatics Engineers

    2025年

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    Japanese Society for Bioinformatics

  • Complete "Materials Data Sciences and Informatics" Course

    2022年

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    Georgia Institute of Technology, Coursera

  • 実験動物2級技術者資格認定試験 合格

    2020年

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    公益社団法人 日本実験動物協会

  • Academic Writing Course, Writing Instructor

    2017年

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    Waseda University Academic Writing Program

  • The Course of Leader for Technology Management

    2017年

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    Department of Business Design and Management, Graduate School of Creative Science and Engineering, Waseda University

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Courses Previously Taught 【 Display / hide 】

  • Information Literacy(Lectures)

    University of Tsukuba

    2025.05
    -
    2025.07

  • Practice in Basic AI-Programming

    Chiba Institute of Technology

    2025.04
    -
    2025.07

  • Special Topics in Applied Physics and Physico-Infomatics

    Keio University

    2025.04
    -
    2025.07

  • Data Science

    University of Tsukuba

    2024.10
    -
    Present

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Social Activities 【 Display / hide 】

  • 量子コンピュータとライフサイエンス：事業にインパクトをもたらす活用領域を探る

    株式会社QunaSys, LINK-J, 

    , 
    2025.10

  • バイオ×計算×量子 異分野を超えた挑戦の今とこれから～民間企業研究者、社会人博士、大学特任教員を経て～

    慶應義塾大学, 物理情報工学特別講義, 

    , 
    2025.05

  • Research and development for the construction of quantum/classical hybrid computing systems

    慶應義塾先端科学技術研究センター(KLL), KEIO TECHNO-MALL 2024, 

    , 
    2024.12

  • Quantum CAE向け量子・AI最適化ソフトウェア

    国立研究開発法人 科学技術振興機構, 大学見本市2024〜イノベーションジャパン, 

    , 
    2024.08

  • 学生向けセミナー「水環境ビジネスガイダンス」

    公益社団法人 日本水環境学会 産官学協力委員会, 第56回日本水環境学会年会, 

    , 
    2022.03

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Memberships in Academic Societies 【 Display / hide 】

  • 日本学校保健学会, 

    2023.04
    -
    2024.03

  • 日本物理学会, 

    2021.11
    -
    Present
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Committee Experiences 【 Display / hide 】

  • 2025.10
    -
    Present

    Member of the Executive Committee, Division 11, The Physical Society of Japan

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