Updated on 2026/04/29

写真a

 
arai shunta
 
Organization
School of Computing Assistant Professor
Title
Assistant Professor
External link

News & Topics

Research Areas

  • Informatics / Soft computing

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

Education

  • 東北大学大学院   情報科学研究科   応用情報科学専攻

    2018.10 - 2021.9

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    Country: Japan

    Notes: 博士課程後期3年の課程

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  • 東北大学大学院   情報科学研究科   応用情報科学専攻

    2017.4 - 2018.9

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    Country: Japan

    Notes: 博士課程前期2年の課程

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  • Tohoku University

    2013.4 - 2017.3

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    Country: Japan

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Research History

  • Institute of Science Tokyo   Department of Mathematical and Computer Science, School of Computing   Assistant Professor

    2024.10

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  • 東京工業大学 情報理工学院 数理・計算科学系 高邉研究室   助教

    2024.4 - 2024.10

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  • 東京工業大学 国際先駆研究機構 量子コンピューティング研究拠点   助教

    2022.4 - 2024.3

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  • 東北大学大学院 情報科学研究科応用情報科学専攻 情報基礎科学専攻 大関研究室   特任助教 (研究)

    2021.10 - 2022.3

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  • Sigma-i   Researcher

    2020.5 - 2022.3

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  • 日本学術振興会特別研究員 DC1)

    2019.4 - 2020.4

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Professional Memberships

Papers

  • Transfer learning for a deep-unfolded combinatorial optimization solver with quantum annealer Reviewed International journal

    Ryo Hagiwara, Shunta Arai, Satoshi Takabe

    Physical Review A   abs/2501.03518   2025.7

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1103/d3sc-3wkj

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  • Quantum annealing enhanced Markov-Chain Monte Carlo Reviewed International journal

    Shunta Arai, Tadashi Kadowaki

    Scientific Reports   15 ( 1 )   2025.7

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1038/s41598-025-07293-y

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  • Deep unfolded local quantum annealing Reviewed International journal

    Shunta Arai, Satoshi Takabe

    Physical Review Research   2024.12

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1103/PhysRevResearch.6.043325

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  • Effectiveness of quantum annealing for continuous-variable optimization Reviewed International journal

    Shunta Arai, Hiroki Oshiyama, Hidetoshi Nishimori

    Physical Review A   2023.10

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1103/PhysRevA.108.042403

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  • Mean-Field Analysis of Sourlas Codes with Adiabatic Reverse Annealing International journal

    Shunta Arai

    Sublinear Computation Paradigm   319 - 334   2021.10

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    Authorship:Lead author   Language:English   Publishing type:Part of collection (book)   Publisher:Springer Singapore  

    Abstract

    In this chapter, we analyze the typical performance of adiabatic reverse annealing (ARA) for Sourlas codes. Sourlas codes are representative error-correcting codes related to p-body spin-glass models and have a first-order phase transition for $$p>2$$, which degrades the estimation performance. In the ARA formulation, we introduce the initial Hamiltonian which incorporates the prior information of the solution into a vanilla quantum annealing (QA) formulation. The ground state of the initial Hamiltonian represents the initial candidate solution. To avoid the first-order phase transition, we apply ARA to Sourlas codes. We evaluate the typical ARA performance for Sourlas codes using the replica method. We show that ARA can avoid the first-order phase transition if we prepare for the proper initial candidate solution.

    DOI: 10.1007/978-981-16-4095-7_13

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  • Dynamical Analysis of Quantum Annealing International coauthorship International journal

    Anthony C. C. Coolen, Theodore Nikoletopoulos, Shunta Arai, Kazuyuki Tanaka

    Sublinear Computation Paradigm   295 - 317   2021.10

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    Language:English   Publishing type:Part of collection (book)   Publisher:Springer Singapore  

    Abstract

    Quantum annealing aims to provide a faster method than classical computing for finding the minima of complicated functions, and it has created increasing interest in the relaxation dynamics of quantum spin systems. Moreover, problems in quantum annealing caused by first-order phase transitions can be reduced via appropriate temporal adjustment of control parameters, and in order to do this optimally, it is helpful to predict the evolution of the system at the level of macroscopic observables. Solving the dynamics of quantum ensembles is nontrivial, requiring modeling of both the quantum spin system and its interaction with the environment with which it exchanges energy. An alternative approach to the dynamics of quantum spin systems was proposed about a decade ago. It involves creating stochastic proxy dynamics via the Suzuki-Trotter mapping of the quantum ensemble to a classical one (the quantum Monte Carlo method), and deriving from this new dynamics closed macroscopic equations for macroscopic observables using the dynamical replica method. In this chapter, we give an introduction to this approach, focusing on the ideas and assumptions behind the derivations, and on its potential and limitations.

    DOI: 10.1007/978-981-16-4095-7_12

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  • Teacher-Student Learning for a Binary Perceptron with Quantum Fluctuations Reviewed

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Journal of the Physical Society of Japan   90 ( 7 )   074002 - 074002   2021.7

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Physical Society of Japan  

    DOI: 10.7566/jpsj.90.074002

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  • Mean field analysis of reverse annealing for code-division multiple-access multiuser detection Reviewed International journal

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Physical Review Research   2021.7

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1103/PhysRevResearch.3.033006

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  • Dynamics of order parameters of nonstoquastic Hamiltonians in the adaptive quantum Monte Carlo method. Reviewed International journal

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Physical review. E   99 ( 3-1 )   032120 - 032120   2019.3

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    We derive macroscopically deterministic flow equations with regard to the order parameters of the ferromagnetic p-spin model with infinite-range interactions. The p-spin model has a first-order phase transition for p>2. In the case of p≥5, the p-spin model with antiferromagnetic XX interaction has a second-order phase transition in a certain region. In this case, however, the model becomes a nonstoquastic Hamiltonian, resulting in a negative sign problem. To simulate the p-spin model with antiferromagnetic XX interaction, we utilize the adaptive quantum Monte Carlo method. By using this method, we can regard the effect of the antiferromagnetic XX interaction as fluctuations of the transverse magnetic field. A previous study [J. Inoue, J. Phys. Conf. Ser. 233, 012010 (2010)1742-659610.1088/1742-6596/233/1/012010] derived deterministic flow equations of the order parameters in the quantum Monte Carlo method. In this study, we derive macroscopically deterministic flow equations for the magnetization and transverse magnetization from the master equation in the adaptive quantum Monte Carlo method. Under the Suzuki-Trotter decomposition, we consider the Glauber-type stochastic process. We solve these differential equations by using the Runge-Kutta method, and we verify that these results are consistent with the saddle-point solution of mean-field theory. Finally, we analyze the stability of the equilibrium solutions obtained by the differential equations.

    DOI: 10.1103/PhysRevE.99.032120

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  • Deep Neural Network Detects Quantum Phase Transition Reviewed

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Journal of the Physical Society of Japan   87 ( 3 )   033001 - 033001   2018.3

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Physical Society of Japan  

    DOI: 10.7566/jpsj.87.033001

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Books

  • 量子コンピュータによる機械学習

    Schuld, Maria, Petruccione, F. (Francesco), 荒井, 俊太, 篠島, 匠人, 高橋, 茶子, 御手洗, 光祐, 山城, 悠, 大関, 真之

    共立出版  2020.8  ( ISBN:9784320124622

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    Total pages:xiv, 347p   Language:Japanese  

    CiNii Books

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MISC

  • Applications and Future Perspectives of Quantum Annealing Invited

    大関真之, 荒井俊太, 観山正道

    電子情報通信学会誌   106 ( 1 )   52 - 57   2023.1

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)  

    CiNii Books

    CiNii Research

    J-GLOBAL

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    Other Link: https://ndlsearch.ndl.go.jp/books/R000000004-I032619072

  • Comparison of quantum and classical optimization algorithms for continuous variable optimization

    日本物理学会講演概要集(CD-ROM)   78 ( 2 )   2023

  • Recent advances of quantum annealing and its typical performance analysis

    荒井俊太

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)   66th   2022

  • Probabilistic information processing with quantum fluctuations

    荒井俊太

    電子情報通信学会大会講演論文集(CD-ROM)   2020   2020

  • Learning of Ising perceptron with quantum fluctuations

    日本物理学会講演概要集(CD-ROM)   75 ( 2 )   2020

  • Statistical mechanical analysis of CDMA multiuser detection with reverse annealing

    荒井俊太, 大関真之, 田中和之

    日本物理学会講演概要集(CD-ROM)   75 ( 1 )   2020

  • マスター方程式を用いた非擬似古典確率的なハミルトニアンの秩序変数のダイナミクス

    荒井俊太, 大関真之, 田中和之

    日本物理学会講演概要集(CD-ROM)   74 ( 1 )   2019

  • D-Waveマシン上における量子相転移の解析

    荒井俊太, 大関真之, 田中和之

    日本物理学会講演概要集(CD-ROM)   73 ( 2 )   2018

  • ニューラルネットワークによる様々な相転移の検出

    荒井俊太, 大関真之, 片岡駿, 田中和之

    日本物理学会講演概要集(CD-ROM)   72 ( 2 )   2017

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Presentations

  • Learning-based quantum optimization algorithms for quadratic unconstrained binary optimization problems, International conference

    Shunta Arai, Ryo Hagiwara, Satoshi Takabe

    INQA 2024 - International Network on Quantum Annealing Conference  2024.10 

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    Language:English   Presentation type:Poster presentation  

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  • ニューラルネットワークによる様々な相転移の検出

    荒井 俊太, 大関 真之, 片岡 駿, 田中 和之

    第20回情報論的学習理論ワークショップ(IBIS2017)  2017.11 

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    Language:Japanese   Presentation type:Poster presentation  

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  • ニューラルネットワークによる様々な相転移の検出

    荒井 俊太, 大関 真之, 片岡 駿, 田中 和之

    日本物理学会秋季大会  2017.9 

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    Language:Japanese   Presentation type:Oral presentation (general)  

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  • Detection of phase transition in transverse-field Ising model by neural network,

    Shunta Arai, Masayuki Ohzeki, Shun Kataoka, Kazuyuki Tanaka

    Adiabatic Quantum Computing Conference (2017)  2017.6 

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  • Adaptive quantum Monte Carlo method for a class of non-stoquastic Hamiltonian by using D-Wave machine

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Qubits 2018  2018.9 

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  • Deep Neural Network detects quantum phase transition in D-Wave 2000Q

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Adiabatic Quantum Computing Conference (2018)  2018.6 

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  • Acceleration of adaptive quantum Monte Carlo Sampling for a class of non-stoquastic Hamiltonian by using D-Wave 2000Q

    Shunta Arai, Shuntaro Okada, Masayuki Ohzeki, Kazuyuki Tanaka

    Adiabatic Quantum Computing Conference (2018)  2018.6 

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    Presentation type:Poster presentation  

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  • ニューラルネットワークによる量子相転移の検出

    荒井俊太, 大関 真之, 田中 和之

    CREST研究集会  2018.3 

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  • Detection of quantum phase transition in D-Wave 2000Q by deep neural network

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Quantum Machine Learning & Biomimetic Quantum Technologies  2018.3 

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  • ニューラルネットワークによる様々な相転移の検出

    荒井 俊太, 大関 真之, 片岡 駿, 田中 和之

    情報系 Winter Festa Episode 3  2017.12 

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    Presentation type:Poster presentation  

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  • Statistical mechanical analysis of reverse annealing for code-division multiple-access multiuser demodulator Invited

    Shunta Arai, Masayuki Ohzeki, Kayuki Tanaka

    Statistical Physics of Disordered Systems and Its Applications (SPDSA2019) --- Statistical-Mechanical Informatics and Statistical Machine Learning Theory in Big Data Sciences  2019.10 

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    Language:English   Presentation type:Oral presentation (invited, special)  

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  • Mean-field analysis of quantum error-correcting codes with non-stoquastic Hamiltonian

    Shunta Arai, Masayuki Ohzeki, Kazuyuki Tanaka

    Adiabatic Quantum Computing Conference (2019)  2019.6 

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  • マスター方程式を用いた非擬似古典確率的なハミルトニアンの秩序変数のダイナミクス

    荒井 俊太, 大関 真之, 田中 和之

    日本物理学会  2019.3 

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    Presentation type:Oral presentation (general)  

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  • Dynamics of order parameters of non-stoquastic Hamiltonian in adaptive quantum Monte Carlo method Invited

    Shunta Arai, Masayuki Ohzeki, Kayuki Tanaka

    Statistical Physics of Disordered Systems and Its Applications (SPDSA2018) --- Statistical-Mechanical Informatics and Statistical Machine Learning Theory in Big Data Sciences  2018.11 

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  • D-Waveマシンにおける量子相転移の解析

    荒井 俊太, 大関 真之, 田中 和之

    日本物理学会  2018.9 

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  • 量子アニーリングの進展と典型性能解析 Invited

    荒井 俊太

    第66回システム制御学会 研究発表講演会  2022.5 

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  • Adiabatic reverse annealing does not overcome the hard phase in CDMA multiuser detection

    Shunta Arai, Masayuki Ohzeki, Kayuki Tanaka

    Adiabatic Quantum Computing Conference 2021  2021.6 

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  • 量子揺らぎによるイジングパーセプトロンの学習

    荒井 俊太, 大関 真之, 田中 和之

    日本物理学会  2020.9 

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  • 量子揺らぎによる確率的情報処理 Invited

    荒井 俊太, 大関 真之, 田中 和之

    電子情報通信学会総合大会  2020.3 

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  • リバースアニーリングによるCDMAマルチユーザ検出の統計力学的解析

    荒井 俊太, 大関 真之, 田中 和之

    日本物理学会  2020.3 

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  • Continuous-variable Optimization by Quantum Annealing, Invited International conference

    Shunta Arai, Hiroki Oshiyama, Hidetoshi Nishimori

    East Asia Joint Seminars On Statistical Physics 2023  2023.10 

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  • 連続変数最適化問題に対する量子・古典最適化アルゴリズムの比較

    荒井 俊太, 押山 広樹, 西森 秀稔

    日本物理学会  2023.9 

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  • Benchmark study of continuous function optimization by a quantum annealer, International conference

    Shunta Arai, Hiroki Oshiyama, Hidetoshi Nishimori

    28th International Conference on Statistical Physics, Statphys28  2023.8 

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  • Quantum annealing for continuous function optimization International conference

    Shunta Arai, Hiroki Oshiyama, Hidetoshi Nishimori

    Adiabatic Quantum Computing Conference 2023  2023.6 

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  • D-Waveマシンによる連続最適化

    荒井俊太

    量子アニーリング及び関連技術の基礎から社会実装まで  2023.2 

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  • 量子揺らぎを用いたイジングパーセプトロンの教師-生徒学習 Invited

    荒井俊太

    量子コンピューティング研究拠点 発足記念講演会  2022.6 

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    Language:Japanese   Presentation type:Oral presentation (invited, special)  

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  • 深層展開の新展開:量子/量子模擬アルゴリズムへの適用

    高邉 賢史, 荒井 俊太, 萩原 涼

    深層展開に基づく信号処理アルゴリズム構築論の深化と展開  2024.9 

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    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

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  • 転移学習に基づく量子アニーラーを利用した深層展開型最適化ソルバーの提案

    萩原 涼, 荒井 俊太, 高邉 賢史

    電子情報通信学会 信号処理研究会  2024.8 

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  • 量子アニーリングを用いたマルコフ連鎖モンテカルロ法の加速

    荒井 俊太, 門脇 正史, 西森 秀稔

    量子アニーリング及び関連技術の基礎から社会実装まで  2024.2 

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  • Deep Unfolded Quantum-Inspired Annealing International conference

    Shunta Arai

    INQA 2023 - International Network on Quantum Annealing Conference  2023.11 

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  • 深層展開を用いた半古典化量子アニーリングの加速

    荒井 俊太, 高邉 賢史

    日本物理学会  2024.9 

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  • 量子アニーラーを用いた深層展開型組合せ最適化ソルバーのための転移学習

    萩原 涼, 荒井 俊太, 高邉 賢史

    日本物理学会  2024.9 

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  • サンプリング過程の微分情報に基づくレプリカ交換法の温度セット最適化日本物理学会

    宮田 竜弥, 荒井 俊太, 高邉 賢史

    日本物理学会  2025.9 

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  • 深層展開型組合せ最適化ソルバーの不等式制約への応用

    萩原 涼, 荒井 俊太, 高邉 賢史

    日本物理学会  2025.9 

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  • 粒子型変分推論を用いたWang-Landau法による周辺尤度比の推定

    依田 和城, 西川 宜彦, 荒井 俊太, 高邉 賢史

    信号処理研究会  2025.6 

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  • Wang-Landau法を用いたグローバー適応探索の効率化

    土橋 慶太, 荒井 俊太, 高邉 賢史

    日本物理学会  2025.9 

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    Country:Japan  

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Awards

  • 研究科長賞

    2019.3   東北大学 大学院情報科学研究科  

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  • 総長賞

    2019.3   東北大学  

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Research Projects

  • データ駆動型量子インスパイアド最適化アルゴリズムの開発と応用

    Grant number:25K21297  2025.4 - 2028.3

    日本学術振興会  科学研究費助成事業  若手研究

    荒井 俊太

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    Grant amount:\4420000 ( Direct Cost: \3400000 、 Indirect Cost:\1020000 )

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  • 量子揺らぎを伴うサンプリングを駆使した革新的機械学習アルゴリズムの創出

    Grant number:19J21790  2019.4 - 2022.3

    日本学術振興会  科学研究費助成事業  特別研究員奨励費

    荒井 俊太

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    Grant amount:\2500000 ( Direct Cost: \2500000 )

    年次計画に記した通り, 量子モンテカルロ法を用いて, 1 次元横磁場イジングモデルのスピン配位から横磁場を学習するニューラルネットワークを作成した. これにより量子アニーラーを用いてサンプリングしたスピン配位を入力にして, 量子アニーラー内のパラメータから横磁場を推定することができた. その横磁場推定器を利用して, 横磁場以外の量子揺らぎをシミュレートする手法である適応的量子モンテカルロ法を量子アニーラー上で動かすアルゴリズムを作成し実装した. また, 通常の古典コンピュータ上で反強磁性相互作用をシミュレートする方法と量子アニーラーを利用してサンプリングする手法との比較を行った. 推定の精度は古典コンピュータに比べ劣るが, サンプリングの速度は量子アニーラーの方が速かった. 横磁場以外の量子揺らぎの有用性を示すために, p体SKモデルに対して横磁場以外の量子揺らぎを導入した量子アニーリングの典型的な性能評価をレプリカ法を用いて行った. その結果, p>2でp体SKモデルが持つ1次相転移が2次相転移になることが分かった. 1次相転移は量子アニーリングにおけるボトルネックであり, 計算時間の指数関数的な増加をもたらす. p体SKモデルの持つ1次相転移は多体相互作用由来である. 磁場由来の1次相転移についても解析を行った. 扱った問題は無線通信技術で使われていた符号分割多重接続(CDMA)モデルである.この問題はユーザー数と観測数の比が過負荷の場合, 1次相転移を持つ. この問題に対して, 横磁場以外の量子揺らぎを導入した場合を解析したところ, この問題の持つ一次相転移は横磁場以外の量子揺らぎでは回避することができないことが分かった.

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Teaching Experience

  • Exercises in Calculus Ⅱ

    2026.10 - 2027.3 Institution:Ochanomizu University

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  • Exercises in Calculus I

    2026.4 - 2026.9 Institution:Ochanomizu University

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  • Fundamentals of Probability.

    2025.6 - 2025.8 Institution:Institute of Science Tokyo

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  • Forum on Computing

    2024.4 Institution:Institute of Science Tokyo

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