Presentations
Invited Talks (International)
[13] N. Yoshioka, APS March Meeting, Minneapolis, US (2024.03.04)
"What is optimal way to mitigate/suppress quantum errors?"
[12] N. Yoshioka, IBM Quantum Summit 2023, New York, US (2023.12.04).
"Laying The Groundwork for Quantum-Powered Use Cases" (Joint presentation)
[11] N. Yoshioka, Computational Approaches to Quantum Many-body systems, Omiya, Japan (2023.10.20).
"Quantum advantage in condensed matter physics"
[10] N. Yoshioka, CCP2023, Kobe, Japan (2023.08.06).
"Hunting for quantum-classical crossover in condensed matter problems"
[9] N. Yoshioka, Asia Pacific QIC Technical Exchange, Online, (2023.04.27).
"Estimating and unblocking path towards quantum advantage"
[8] N. Yoshioka, IBM Quantum Summit 2022, New York, US (2022.11).
"A No-Nonsense Path to Quantum Advantage" (Joint presentation with IBM Quantum)
[7] N. Yoshioka, Rigorous statistical mechanics and related topics, Kyoto, Japan (2022.11).
"Neural-net representation of quantum many-body states"
[6] N. Yoshioka, Variational Learning for Quantum Matter, Lausanne, Switzerland (2022.07).
"Variational Representations in Noisy Quantum States"
[5] N. Yoshioka, Quantum Techniques in Machine Learning, Online meeting (2021.11).
"Advancing Classical and Quantum Variational Algorithms for Many-body Problems"
[4] N. Yoshioka, Recent progress in theoretical physics based on quantum information theory, Kyoto University, Japan (2021.03).
"Advancing variational algorithms for quantum many-body problems"
[3] N. Yoshioka, Machine learning for Quantum Simulation, Flatiron Institute, New York, US (Zoom) (2020.6).
"Solving dissipative many-body system by neural quantum states"
[2] N. Yoshioka, Mini-Workshop on Quantum Optimization, Kanagawa, Japan (2020.3).
“Neural Networks in Open Quantum System” (workshop cancelled)
[1] N. Yoshioka, 5th Conference on Condensed Matter Physics, Liyang, China (2019.6).
“Approximate and exact representation of physical states by neural networks”
Invited Talks (Domestic)
[11] 吉岡信行, 物性研究のための量子アルゴリズム最前線, 大阪, 日本 (2023.03).
"物性物理における量子超越性の探究"
[10] 吉岡信行, 日本物理学会 2023年春季大会, 一般シンポジウム講演, オンライン (2023.03).
"計算リソース推定を通じた物性物理における量子古典クロスオーバー探索"
[9] 吉岡信行, 第2回量子ソフトウェアワークショップ, 東京, 日本 (2023.01).
"量子誤り抑制の進展と展開"
[8] N. Yoshioka, Stat&QuantPhys Autumn School 2022, オンライン (2022.09).
"How to Encode Quantum Many-body Physics into Neural Networks" (Lecture in english)
[7] 吉岡信行, 第9回統計物理学懇談会, オンライン (2022.03).
"量子多体問題における古典/量子変分探索"
[6] 吉岡信行, 第3回冷却原子研究会「アトムの会」, オンライン (2021.08).
"ニューラルネットワークによる量子多体物性の探索"
[5] 吉岡信行, 第35回人工知能学会全国大会, オンライン (2021.06).
"量子多体系に機械学習アプローチで挑む"
[4] 吉岡信行, 理論研究会「量子多体系の相形成とダイナミクス」, Zoom (2021.4).
“量子多体系をニューラルネットワークに埋め込む"
[3] 吉岡信行, 76th Annual meeting of Japanese Physical Society, Japan (Zoom) (2021.03).
"Progress and prospects for theoretical studies of physical states using neural networks"
(ニューラルネットワークによる物理状態の理論研究における進展と展望)
[2] 吉岡信行, Frontiers of Quantum Computational Science, UTokyo, Japan (Zoom) (2020.07).
"Neural Networks as Quantum Many-body Solver" (開放量子多体系ソルバとしての機械学習関数)
[1] 吉岡信行, Young Scientist Award Speech for Japanese Physical Society, Nagoya, Japan (2020.3).
“ニューラルネットワークによる物理状態の分類と表現に関する理論的研究"
(Theoretical study on the classification and description of physical states by means of neural networks)
Seminars
[21] 吉岡信行, QIIセミナー, Zoom webinar (2023.04.25).
"物性物理における量子超越性の探究"
[20] 吉岡信行, QPARC, Zoom webinar (2022.12).
"Hunting for quantum-classical crossover in condensed matter problems"
[19] 吉岡信行, 第25回 Beyond AIセミナー, Zoom webinar (2022.04).
"量子多体状態としてのニューラルネットワーク"
[18] 吉岡信行, Q-LEAP 量子AIセミナー, Zoom webinar (2021.07).
"ニューラルネットワークで探る量子多体物理"
[17] N. Yoshioka, Online CMT Seminar, Zoom webinar (2021.07).
"ニューラルネットワークによる固体量子物性の第一原理計算"
[16] N. Yoshioka, ipi seminar, University of Tokyo, Zoom webinar (2021.05).
"Encoding many-body quantum physics into neural networks"
[15] N. Yoshioka, 第27回関西量子情報Student Chapter, Zoom Webinar (2020.12).
"量子状態の機械学習" (Machine Learning Quantum States)
[14] N. Yoshioka, Deep learning and Physics 2020, Zoom webinar (2020.07).
"ニューラルネットワークで探る量子多体系の表現"
[13] N. Yoshioka, Yagami Statistical Physics Seminar, Zoom webinar (2020.07) .
"Quantum Many-body Simulation by Neural Networks"
[12] N. Yoshioka, Sagawa group informal seminar, Zoom webinar (2020.06).
"Neural Networks for Open Quantum Many-body Systems"
[11] N. Yoshioka, Quantum Computational Materials Science Roundtable, Zoom webinar (2020.06).
"Quantum and Classical Variational Algorithms for Many-body Problems" (量子多体系のための変分アルゴリズム)
[10] N. Yoshioka, ASRC Seminar, JAEA, Ibaraki, Japan (2019.11.17)
"ニューラル物性物理"
[9] N.Yoshioka, Informal Seminar, University of Tokyo, Tokyo, Japan (2019.9.19)
"Designing neural networks for representation of many-body spin systems"
[8] N. Yoshioka, Theoretical Quantum Physics Laboratory Seminar, RIKEN, Saitama, Japan (2019.7.22)
"Designing neural networks for stationary states in open quantum many-body systems"
[7] N. Yoshioka, 『Graph Neural Networkの最前線』,Tokyo, Japan (2019.6.11)
"物性物理における機械学習"
[6] N.Yoshioka, TQM seminar, OIST, Okinawa, Japan (2019.4.17) [Abstract]
"Representation of Quantum Many-body Systems by Neural Networks"
[5] N. Yoshioka, MIT seminar, University of Tokyo, Japan (2018.12). [Abstract]
“Transforming Generalized Ising Model into Boltzmann Machine”
[4] N. Yoshioka, Condensed Matter Seminar, Tsukuba University, Ibaraki, Japan (2018.6). [Abstract]
“Machine Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[3] N. Yoshioka, Seminar in Solid State Physics, University of Zurich, Zurich, Switzerland (2018.2). [Abstract]
“Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[2] N. Yoshioka, Hadron Physics Group Seminar, University of Tokyo, Tokyo, Japan (2017.12).
“Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[1] N. Yoshioka, Informal Condensed Matter Seminar, Kyoto University, Kyoto, Japan (2017.11).
“Machine Learning Crash Course for Condensed Matter Physicists”
Oral Presentation (International)
[12] T. Yada, N. Yoshioka, T. Sagawa, Quantum Information Entropy in Physics, Kyoto, Japan (2022.03).
"Quantum Fluctuation Theorem under Continuous Measurement and Feedback"
[11] N. Yoshioka, H. Hakoshima, Y. Matsuzaki, Y. Tokunaga, Y. Suzuki, S. Endo, APS March meeting 2022, online, US (2022.03).
"Framework of Generalized Quantum Subspace Expansion Method"
[10] N. Yoshioka, W. Mizukami, and F. Nori, Pacifichem 2021, online, US (2021.12).
"Simulating quasiparticle band spectra of real solids by neural-network quantum states"
[9] N. Yoshioka, W. Mizukami, and F. Nori, Conference on Computational Physics (CCP) 2021, online, UK (2021.08).
"Encoding solid-state electronic structures in neural-network quantum states"
[8] N. Yoshioka and Y. O. Nakagawa, K. Mitarai, and K. Fujii, APS March Meeting, Denver, US (2020.3).
"Variational Quantum Algorithm for Markovian Open Quantum Systems"
[7] N. Yoshioka and R. Hamazaki, APS March Meeting, Denver, US (2020.3).
"Designing neural networks for stationary states in open quantum many-body systems "
[6] N. Yoshioka, Y. Akagi, and H. Katsura, StatPhys27, Buenos Aires, Argentina (2019.7). [Abstract]
“Transforming Generalized Ising Model into Boltzmann Machine”
[5] Y. Akagi, N. Yoshioka, and H. Katsura, APS March Meeting, Boston, US (2019.3).
“Detection of Phase Transitions in Quantum Spin Chains via Unsupervised Machine Learning”
[4] N. Yoshioka, Y. Akagi, and H. Katsura, Machine Learning for Quantum Many-body Physics, Dresden, Germany (2018.6).
“Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[3] N. Yoshioka, Y. Akagi, and H. Katsura, APS March Meeting, Los Angels, US (2018.3).
“Machine Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[2] N. Yoshioka, Y. Akagi, and H. Katsura, Novel Quantum States in Condensed Matter 2017 (NQS2017), Kyoto, Japan (2017.10).
“Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[1] N. Yoshioka, Y. Akagi, and H. Katsura, Machine Learning and Many-Body Physics, Beiijng, China (2017.7).
“Machine learning phases of disordered topological superconductors”
Poster Presentation (International)
[11] N. Yoshioka and R. Hamazaki, Deep Learning And Physics 2019, Kyoto, Japan (2019.10).
"Constructing neural stationary states for open quantum many-body systems"
[10] N. Yoshioka, R. Hamazaki, Engineering Nonequilibrium Dynamics of Open Quantum Systems, Dresden, Germany (2019.6).
"Constructing neural stationary states for open quantum many-body systems”
[9] N. Yoshioka, Y. O. Nakagawa, K. Mitarai, K. Fujii, TQC 2019 + NISQ, Maryland, US (2019.6).
"dVQE: dissipative-system Variational Quantum Eigensolver"
[8] N. Yoshioka and R. Hamazaki, FCES19, Tokyo, Japan (2019.5).
"Constructing neural stationary states for open quantum many-body systems”
[7] N. Yoshioka, Y. Akagi, and H. Katsura, At the Crossroad of Physics and Machine Learning, Santa Barbara, US (2019.2).
“Transforming Generalized Ising Model into Boltzmann Machine”
[6] N. Yoshioka, Y. Akagi, and H. Katsura, Machine Learning for Quantum Many-body Physics, Dresden, Germany (2018.6).
“Cluster updating classical spin systems by equivalent Boltzmann machines”
[5] N. Yoshioka, Y. Akagi, and H. Katsura, FOR1807 Winter School on Numerical Methods for Strongly Correlated Quantum Systems, Marburg, Germany (2018.2).
“Learning Disordered Topological Phases by Statistical Recovery of Symmetry”
[4] N. Yoshioka and M. Sigrist, International Conference on Topological Materials Science 2017 (TopoMat 2017), Tokyo, Japan (2017.5).
“Anomalous Thermal Hall Effect in Nodal Chiral Superconductors”
[3] N. Yoshioka and M. Sigrist, 15th Condensed Matter Days of the French Physical Society, Bordeaux, France (2016.8).
“Nodal Effects on Thermal Hall Conductance in Chiral Superconductors”
[2] N. Yoshioka, T. Ideue, T. Kurumaji, and H. Katsura, Asia-Pacific Workshop (APW)-CEMS Joint Workshop, Tokyo, Japan (2016.1).
“Anomalous phonon Hall effect in polar ferrimagnets”
[1] N. Yoshioka, H. Matsuura, and M. Ogata, International Workshop on Dirac Electrons in Solids 2015, Tokyo, Japan (2015.1).
“Dirac cones in Hofstadter butterfly”
国内発表
招待講演
[2] 吉岡信行, Frontiers of Quantum Computational Science, 東京大学物性研究所, Zoom (2020.07).
"開放量子多体系ソルバとしての機械学習関数"
[1] 吉岡信行, 日本物理学会第75回年次大会 若手奨励賞受賞講演, 名古屋, 日本 (2020.3).
“ニューラルネットワークによる物理状態の分類と表現に関する理論的研究”
口頭発表 (国内)
[12] 前蔵遼、鈴木泰成、吉岡信行、徳永裕己、第6回量子ソフトウェア研究会, zoom (2022.07).
「生成モデルを用いた量子状態トモグラフィーに基づくノイズレスな期待値の推定」
[11] 大倉 康寛、遠藤 傑、吉岡 信行、第6回量子ソフトウェア研究会 (2022.07)
"Generalized quantum subspace expansion utilizing hardware-control imperfection"
[10] 前蔵遼、鈴木泰成、吉岡信行、徳永裕己、量子情報技術研究会, zoom (2022.05).
「生成モデルを用いた量子状態トモグラフィーに基づくノイズレスな期待値の推定」
[9] I. Hamamura, H. Horii Hiroshi, T. Imamichi, J. Doi, N. Yoshioka, S. Seelam, T. Sagawa, A. Mezzacapo, 第5回量子ソフトウェア研究会、zoom (2022.03).
"Parallel grouping algorithm for a large set of Pauli operators"
[8] 吉岡信行、箱嶋秀昭、松崎雄一郎、徳永祐己、鈴木泰成、遠藤傑、第4回量子ソフトウェア研究会、zoom (2021.10).
「Generalized Quantum Subspace Expansion Method for Error Mitigation」
[7] 赤城裕、吉岡信行、桂法称、日本物理学会第74回年次大会(2019年)、福岡 (2019.3)。
「教師なし学習による量子スピン鎖の相転移検出」
[6] 吉岡信行、赤城裕、桂法称、日本物理学会2018年秋季大会、奈良 (2018.9)。
「古典スピン系と等価なボルツマン機械への変換と大域的更新法」
[5] 吉岡信行、赤城裕、桂法称、日本物理学会2017年秋季大会、岩手 (2017.9)。
「機械学習による乱れたトポロジカル超伝導体の相判定」
[4] 吉岡信行、赤城裕、桂法称、日本物理学会2017年秋季大会、岩手 (2017.9)。
「乱れたトポロジカル超伝導体の非可換指数と量子相図」
[3] 吉岡信行、Manfred Sigrist、日本物理学会2016年秋季大会、金沢 (2016.9)。
「トポロジカル超伝導体におけるノード構造と熱ホール効果」
[2] 吉岡信行、松浦弘康、小形正男、日本物理学会2015年秋季大会、大阪 (2015.9)。
「Hofstadter’s butterfly中のmassless Dirac fermionとfree fermion」
[1] 吉岡信行、第60回物性若手夏の学校分科会、岐阜 (2015.8)。
「Hofstadter’s butterflyとその性質」
ポスター発表 (国内)
[4] 吉岡信行、濱崎立資、第13回物性科学領域横断研究会、東京(2019.11)。
「ニューラルネットワークによる量子開放系の定常状態の表現 」
[3] 吉岡信行、赤城裕、桂法称、第11回物性科学領域横断研究会、千葉 (2017.11)。
「対称性の統計的回復による乱れたトポロジカル相の学習と分類」
[2] 吉岡信行、井手上敏也、車地崇、桂法称、新学術領域研究「トポロジーが紡ぐ物質科学のフロンティア」第1回領域研究会、東京 (2015.12)。
「極性フェリ磁性体におけるフォノンの異常熱ホール効果」
[1] 海老原周*、吉岡信行*、関口文哉、松永隆佑、島野亮、第6回低温センター研究交流会、東京 (2015.1)。
「テラヘルツ強電場によるバルクGaAs中励起子のイオン化機構の研究」
(*These authors contributed equally to this work)