DEEP-INセミナー
36 イベント
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セミナー
DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
2026年6月4日(木) 15:00 - 16:00
Wei-Lin Wu (Ph.D. Student, School of Physics, Peking University, China)
Recent discoveries of multiquark candidates have opened a new frontier in hadron spectroscopy and nonperturbative QCD. Understanding these multiquark states poses a challenging quantum many-body problem governed by SU(3) color interactions. Traditional approaches based on basis expansions often encounter severe bottlenecks as the system size and dynamical complexity increase. In this talk, I will present DeepQuark, a deep-neural-network-based variational Monte Carlo framework for solving multiquark bound states. I will discuss the general methodology behind neural-network quantum states, the challenges of extending existing approaches from electronic and nuclear systems to hadron physics, and the architecture of DeepQuark. By combining physics-informed symmetry constructions with the expressive power of deep neural networks, DeepQuark provides a scalable framework for studying multiquark spectroscopy and exploring confinement dynamics.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Generative diffusion model with inverse renormalization group flows
2026年6月2日(火) 14:00 - 15:00
増木 貫太 (東京大学 大学院理学系研究科 博士課程)
Diffusion models have recently emerged as one of the most powerful frameworks for generative modeling, achieving remarkable success in a wide range of domains, including image generation, audio synthesis, and scientific data generation. However, despite their empirical success, conventional diffusion models often require many denoising steps and do not explicitly exploit the multiscale structure naturally present in various types of data. This limitation motivates us to ask whether ideas from the renormalization group (RG), which is designed to describe scale-dependent effective degrees of freedom, can provide a useful principle for constructing more efficient generative models. In this talk, I will present our recent work on renormalization-group diffusion models (RGDMs) [1], a generative framework that connects diffusion models with RG flows. By establishing a correspondence between diffusion dynamics and exact RG flow equations, we construct a diffusion model whose reverse process generates data in a coarse-to-fine manner, thereby effectively reversing an RG flow. I will first introduce the theoretical formulation of RGDMs and explain how the RG perspective leads to a coarse-to-fine generative process. I will then present numerical results in protein structure prediction and image generation, where RGDMs improve sample quality and/or sampling efficiency compared with conventional diffusion models. Finally, I will discuss possible extensions and open questions, including broader applications of RG-inspired generative modeling.
会場: セミナー室 (359号室)
イベント公式言語: 英語
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セミナー
Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation
2026年5月14日(木) 14:30 - 15:30
ジアン・シュウ (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 量子数理科学チーム 特別研究員)
In quantum machine learning (QML), classical data are often encoded as quantum pure states and processed directly as quantum representations, motivating \emph{representation-level generative modeling} that samples new quantum states from an underlying pure-state ensemble rather than re-preparing them from perturbed classical inputs. However, extending \emph{score-based} diffusion models with well-defined reverse-time samplers to quantum pure-state ensembles remains challenging, due to the non-Euclidean geometry of the complex projective space $\mathbb{CP}^{d-1}$ and the intractability of transition densities. We propose \emph{Stochastic Schr\"odinger Diffusion Models} (SSDMs), an intrinsic score-based generative framework on $\mathbb{CP}^{d-1}$ endowed with the Fubini--Study (FS) metric. SSDMs formulate a forward Riemannian diffusion with a stochastic Schr\"odinger equation (SSE) realization, and derive reverse-time dynamics driven by the Riemannian score $\nabla_{\mathrm{FS}} \log p_t$. To enable training without analytic transition densities, we introduce a local-time objective based on a local Euclidean Ornstein--Uhlenbeck approximation in FS normal coordinates, yielding an analytic teacher score mapped back to the manifold. Experiments show that SSDMs faithfully capture target pure-state ensemble statistics, including observable moments, overlap-kernel MMD, and entanglement measures, and that SSDM-generated quantum representations improve downstream QML generalization via representation-level data augmentation.
会場: セミナー室 (359号室) 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Building autonomous AI physicists for frontier physics research
2026年4月30日(木) 15:00 - 16:00
Tingjia Miao (Ph.D. Student, School of Artificial Intelligence, Shanghai Jiao Tong University, China)
Advances in LLMs have led to agents with knowledge and operational capabilities comparable to human scientists, suggesting potential to assist, accelerate, and automate research. Physics, especially theoretical and computational physics, which requires integrating analytical reasoning, code-based computation, and profound domain expertise, is well suited for verifying the end-to-end research capabilities of AI scientists. Accordingly, we construct a general-purpose AI physicist PhysMaster, equipped with a layered academic knowledge base, adapted to the agent skill ecosystem, and adopting an adaptive exploration strategy that balances efficiency and exploration, enabling robust performance in ultra-long-horizon tasks; PhysMaster has been open-sourced. Meanwhile, we introduce PRL-Bench (Physics Research by LLMs), a benchmark with 100 tasks adapted from recent Physical Review Letters papers, covering astrophysics, condensed matter physics, high-energy physics, quantum information, and statistical physics. Evaluation across frontier models shows that failures are dominated by conceptual and formulaic errors, and that exploration and derivations remain unstable over long horizons. In addition, we develop domain-specialized AI scientists, including LQCD Master, which integrates Lattice QCD workflows and expert skills, enabling automated generation and submission of lattice computation scripts from concise physics goals.
会場: via Zoom
イベント公式言語: 英語
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セミナー
DEEP-IN WG Sarter Meeting 2026
2026年4月27日(月) 15:30 - 17:00
Tae-Geun Kim (Postdoc, Fudan University, China)
Yang-Yang Tan (東京大学 大学院理学系研究科 知の物理学研究センター 博士研究員)
瀧 雅人 (立教大学 大学院人工知能科学研究科 准教授)15:30–16:00 NOW&NEXT of DEEP-IN WG (Lingxiao Wang) Self-Introduction of Members 16:00–16:20 AI Team and DEEP-IN (Masato Taki) 16:20–16:40 Inverse Problems in HEP (Tae-Geun Kim, FudanU) 16:40–17:00 Inverse Modeling Distributions (Yang-yang Tan, UTokyo)
会場: セミナー室 (359号室) 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Quantum generative learning via diffusion
2026年4月21日(火) 10:00 - 11:00
Zhang Bingzhi (PostDoc, University of Southern California, USA)
Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Generative models for quantum data offer a promising route toward learning and preparing complex quantum-state ensembles. In this talk, I will introduce the quantum denoising diffusion probabilistic model (QuDDPM) [1], which adapts the diffusion-model idea to quantum systems through a forward randomization process and a trainable backward denoising dynamics. I will discuss how this framework enables stepwise learning of target quantum state ensembles and demonstrate its capabilities in various learning tasks. I will then present its extension to mixed states to eliminate the need for scrambling [2]. I will conclude with a brief discussion of recent results on scaling laws of quantum information lifetime in monitored quantum dynamics, emphasizing how mid-circuit measurements can maintain information and provide useful intuition for measurement-assisted quantum machine learning.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Searching For Anomalies with Foundation Models
2026年4月16日(木) 14:00 - 15:30
Vinicius Massami Mikuni (名古屋大学 素粒子宇宙起源研究所 (KMI))
This is a joint seminar with Institute for Physics of Intelligence (iπ), UTokyo Anomaly detection relaxes the assumptions of how new physics should look and extends the reach of what we can discover. However, interpreting the data and estimating backgrounds remains a challenge. In this new work, we investigate anomalous events selected by the OmniLearned Foundation model across different model sizes, performing a full analysis using CMS Open Data. Surprisingly, models of different sizes, trained on the same data with the same loss functions, select entirely different collisions. In particular, the large OmniLearned model (500M parameters) selects events that are not well described by our background model.
会場: 東京大学 本郷キャンパス 理学部１号館 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
DEEP-IN-iPI Joint Meeting
2026年1月26日(月) - 30日(金)
郭 星雨 (Lecturer, Institute of Quantum Matter, South China Normal University, China)
Gert Aarts (Professor, Department of Physics, Swansea University, UK)
施 舒哲 (Assistant Professor, Physics Department, Tsinghua University, Beijing, China)
Sung Hak Lim (Senior Researcher, Center for Theoretical Physics of the Universe (CTPU-PTC), Institute for Basic Science (IBS), Republic of Korea)
ジンヤン・リー (総合研究大学院大学 素粒子原子核コース 博士課程)
許 インイン (Research fellow, Department of Mathematics and Statistics, University of Helsinki, Finland)The series of DEEP-IN meetings (Jan 26–30, 2026) are joint with UTokyo Institute for Physics of Intelligence (iπ), which is a multi-day scientific program bringing together researchers to explore quantum simulations, machine learning physics, and applications in particle and nuclear physics. The tentative schedule is, UTokyo-iπ Session (Venue: #512, Faculty of Science Bldg.1, School of Science, UTokyo) Day 1: Jan 26 (Mon) 14:30–16:00 Onset of Bjorken flow in a quantum many-body simulation of the massive Schwinger model, Shuzhe Shi Day 2: Jan 27 (Tue) 14:30–16:00 Physics of Diffusion Models, Gert Aarts 16:00–17:30 Discovering Symmetry from Energy-Based Diffusion Models, Jinyang Li RIKEN-iTHEMS Session (Venue: Seminar Room #359, Main Research Building) Day 3: Jan 28 (Wed) 14:30–16:00 Understanding Galactic Dark Matter with Generative Models, Sung Hak Lim 16:00–18:00 Free Discussion ML Physics-1 Day 4: Jan 29 (Thu) 10:00–11:30 Quantum Simulations of HEP and Beyond, Xingyu Guo 14:30–16:00 Physics of Machine Learning, Gert Aarts 16:00–17:30 Storage capacity of perceptron with variable selection, Yingying Xu Day 5: Jan 30 (Fri) 11:00–14:00: Free Discussion ML Physics-2
会場: via Zoom / セミナー室 (359号室) / 東京大学 本郷キャンパス 理学部１号館
イベント公式言語: 英語
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セミナー
Neural Network Quantum States for Quarkonium in Medium: Real-Time Open Quantum-System Dynamics
2025年12月19日(金) 16:00 - 17:00
Tom Magorsch (Ph.D. Student, Department of Physics, Technical University of Munich, Germany)
Many phenomena in high energy physics can not be described by Euclidean-time Monte Carlo estimates alone, but require genuine real-time evolution and a treatment of non-equilibrium effects. However, such simulations are computationally challenging. One such example is the evolution of heavy quarkonium in the quark gluon plasma produced in heavy-ion collisions. In this talk, I will introduce the open quantum system treatment of in-medium quarkonium. I will then give an overview on neural network quantum states as a variational approach to the real-time simulation of open quantum systems. As a controlled benchmark system, I will study the application to the Caldeira-Leggett model and conclude with an outlook on future applications of neural network based simulation of quarkonia in medium.
会場: via Zoom
イベント公式言語: 英語
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セミナー
A bi-fidelity Asymptotic-Preserving Neural Network approach for multiscale kinetic problems
2025年12月17日(水) 11:00 - 12:00
Liu Liu (Assistant Professor, Department of Mathematics, The Institute of Mathematical Sciences, The Chinese University of Hong Kong, Hong Kong)
In this talk, we will introduce a bi-fidelity Asymptotic-Preserving Neural Network (BI-APNNs) framework, designed to efficiently solve forward and inverse problems for the linear Boltzmann equation. Our approach builds upon the previously studied Asymptotic-Preserving Neural Network (APNNs), which employs a micro-macro decomposition to handle the model’s multiscale nature. We specifically address a bottleneck in the original APNNs: the slow convergence of the macroscopic density in the near fluid-dynamic regime. This strategy significantly accelerates the training convergence as well as improves the accuracy of the forward problem solution, particularly in the fluid-dynamic limit. We show several numerical experiments on both linear Boltzmann and the Boltzmann-Poisson system that this new BI-APNN method produces more accurate and robust results for forward and inverse problems compared to the standard APNNs. This is a joint work with Zhenyi Zhu and Xueyu Zhu.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Generative sampling with physics-informed kernels
2025年12月8日(月) 14:00 - 15:00
Renzo Kapust (Ph.D. Student, Institute for Theoretical Physics, University Heidelberg, Germany)
We construct a generative network for Monte-Carlo sampling in lattice field theories and beyond, for which the learning of layerwise propagation is done and optimised independently on each layer. The architecture uses physics-informed renormalisation group flows that provide access to the layerwise propagation step from one layer to the next in terms of a diffusion equation for the respective renormalisation group kernel through a given layer. Thus, it transforms the generative task into that of solving once the set of independent and linear differential equations for the kernels of the transformation. As these equations are analytically known, the kernels can be refined iteratively. This allows us to structurally tackle out-of-domain problems generally encountered in generative models and opens the path to further optimisation. We illustrate the practical feasibility of the architecture within simulations in scalar field theories.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Hamiltonian Learning and Dynamics Prediction via Machine Learning
2025年11月26日(水) 15:00 - 16:00
李 可仁 (Assistant Professor, College of Physics and Optoelectronic Engineering, Shenzhen University, China)
Accurate prediction of quantum Hamiltonian dynamics and identification of Hamiltonian parameters are crucial for advancements in quantum simulations, error correction, and control protocols. This talk introduces a machine learning model with dual capabilities: it can deduce time-dependent Hamiltonian parameters from observed changes in local observables within quantum many-body systems, and it can predict the evolution of these observables based on Hamiltonian parameters. The model’s validity was confirmed through theoretical simulations across various scenarios and further validated by two experiments. Initially, the model was applied to a Nuclear Magnetic Resonance quantum computer, where it accurately predicted the dynamics of local observables. The model was then tested on a superconducting quantum computer with initially unknown Hamiltonian parameters, successfully inferring them. We believe that machine learning techniques hold great promise for enhancing a wide range of quantum computing tasks, including parameter estimation, noise characterization, feedback control, and quantum control optimization.
会場: via Zoom
イベント公式言語: 英語
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セミナー
On the Role of Hidden States of Modern Hopfield Network in Transformer
2025年11月10日(月) 14:00 - 15:00
瀧 雅人 (立教大学 大学院人工知能科学研究科 准教授)
Large language models such as ChatGPT are based on deep learning architectures known as Transformers. Owing to their remarkable performance and broad applicability, Transformers have become indispensable in modern AI development. However, it still remains an open question why Transformers perform so well and what the essential meaning of their unique structure is. One possible clue lies in the mathematical correspondence between Hopfield Networks and Transformers. In this talk, I will first introduce the major developments over the past decade that have significantly increased the storage capacity of Hopfield Networks. I will then review the theoretical correspondence between Hopfield Networks and Transformers. Building on this background, I will present our recent findings: by extending this correspondence to include the hidden-state dynamics of Hopfield Networks, we discovered a new class of Transformers that can recursively propagate attention-score information across layers. Furthermore, we found, both theoretically and experimentally, that this new Transformer architecture resolves the “rank collapse” problem often observed in conventional multi-layer attention. As a result, when applied to language generation and image recognition tasks, it achieves performance surpassing that of existing Transformer-based models.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Quantum multi-body problems using unsupervised machine learning
2025年11月5日(水) 15:00 - 16:00
内藤 智也 (東京大学 大学院工学系研究科 原子核国際専攻 特任助教)
I will introduce the recent development of a method to calculate the (anti)symmetrized wave functions and energies of the ground and low-lying excited states using the unsupervised machine learning technique. I will also introduce the recent attempts to consider the spin-isospin degrees of freedom and extend them to the Dirac equation.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Neural network wavefunctions for SU(2) lattice gauge theory in the Hamiltonian formulation
2025年10月22日(水) 15:00 - 16:30
Tom Spriggs (PostDoc, Kavli Institute of Nanoscience and QuTech, Delft University of Technology, Netherlands)
In this talk I will cover our recent preprint arXiv:2509.12323 where we propose a neural network approach to finding the ground state wavefunction of SU(2) lattice gauge theory. Specifically, we demonstrate that the use of bespoke SU(2)-gauge-equivariant neural network layers increases the extent to which our variational ansatz can represent the ground state of this system. During this talk I will contrast the Hamiltonian and Euclidean formalisms of lattice gauge theories, highlighting the promises that the former offers but also the difficulties: noting briefly the issues of parameterising the continuous Hilbert space that plague tensor network and quantum simulation approaches and how our approach alleviates this. I will try and present our method pedagogically as we are very interested in learning its uses but also the limits of its validity, before closing with some remarks on scaling to larger systems and different gauge groups.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Discovering and harnessing symmetry with machine learning
2025年10月6日(月) 16:00 - 17:30
Escriche Santos Eduardo (Ph.D. Student, Department of Computer Science, Technical University of Munich, Germany)
Incorporating symmetry-inspired inductive biases into machine learning models has led to many significant advances in the field, especially for its application to scientific data. However, recently, a trend has emerged that favors implicitly learning relevant symmetries from data instead of designing constrained equivariant architectures. In this talk, I will first introduce these different modelling alternatives, together with their associated benefits and limitations. Then, I will describe some examples of automatic symmetry discovery methods as a way of mitigating some of those limitations. Finally, I will present our recent work that integrates symmetry discovery and the definition of an equivariant model into a joint learnable end-to-end approach, which further alleviates some of the limitations of current equivariant modelling approaches.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Statistical Physics of In-Context Learning in Transformer
2025年9月16日(火) 15:00 - 16:30
Haiping Huang (Professor, School of Physics, Sun Yat-sen University, China)
The pre-trained large model demonstrates the ability to learn from examples, that is, it can infer patterns and generalize from a small number of examples without retraining. How does this ability emerge? This report proposes a physical model mapping of the large model pre-training process, and finds that the training process corresponds to spin condensation, the unique energy ground state will determine the example generalization ability, and the diversity of training data is a key element in algorithm design. This study also reveals that the reasoning process of the large model may be fundamentally different from human thinking.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Neural Network for Holographic QCD
2025年9月12日(金) 10:30 - 11:30
ホーンアン・ツオン (Ph.D. Candidate, College of Physics, Jilin University, China)
Holographic QCD provides a powerful theoretical framework for investigating the equation of state of boundary field theories, where the idea is that the boundary dynamics can be fully determined by solving the bulk equations of motion. However, the coupling functions in the action typically rely on external inputs (such as lattice QCD data), and their explicit forms are often based on artificial assumptions. To eliminate such arbitrariness, we introduce neural networks into the potential reconstruction framework to represent the coupling functions, thereby constructing a fully data-driven machine learning model governed solely by boundary field theory inputs. The results obtained after training show remarkable consistency with the coupling functions derived from holographic renormalization based on prior assumptions, highlighting the strong function-approximation capability of neural networks and revealing the potential to unify the potential reconstruction and holographic renormalization approaches within a common framework.
会場: セミナー室 (359号室) 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Femtoscopy: Probing Fundamental Matter Properties at the Fermi Scale
2025年8月22日(金) 13:30 - 15:00
Yijie Wang (Postdoctoral Researcher, Tsinghua University, Beijin, China / 理化学研究所 仁科加速器科学研究センター (RNC) RI物理研究室 客員研究員)
Femtoscopy, a cutting-edge technique grounded in intensity interferometry (correlation function analysis), enables in-depth exploration of fundamental properties of matter, including space, time, and interactions, at the Fermi scale. Originating from the Hanbury Brown and Twiss (HBT) correlation function, which was utilized in 1956 to measure the angular radius of Sirius, this method has been extended to the subatomic realm, emerging as a pivotal tool for deciphering space-time structures and particle interactions. This talk focuses on three representative femtoscopy studies: First, by combining femtoscopic interferometry with optical deblurring algorithms, it reveals the non-Gaussian freeze-out spatial distribution of protons and antiprotons in Au+Au relativistic heavy-ion collisions, challenging conventional wisdom [Chinese Physics Letters 42, 031401 (2025)]. Second, in the 30 MeV/u ⁴⁰Ar + ¹⁹⁷Au reaction, femtoscopy is employed to determine the proton emission timescale at approximately 100 fm/c and uncover the kinetic law of preferential emission of neutron-rich particles, making an “ultra-fast” video for heavy-ion collisions [Physics Letters B, 825, 136856 (2022)]. Third, using a high-resolution neutron array, femtoscopy accurately measures the neutron-neutron scattering length and effective range, as well as the space-time size of the neutron emission source, providing crucial data for the study of charge symmetry breaking in nuclear forces and nuclear symmetry energy [Physical Review Letter, 134, 222301 (2025)]. These achievements fully demonstrate the significant value of femtoscopy in advancing the frontiers of nuclear and particle physics, spanning from experimental observations to theoretical modeling. Dr. Yijie Wang is the Post Doc of Tsinghua University and Visiting Scientist of RIKEN. He studied physics at Jilin University, China, and obtained his Ph. D. degree at Tsinghua University in 2021. Then, he continued researches in Tsinghua University as Post Doc up to now. His interests focus on heavy ion collision experiment, nuclear equation of state, advanced detection system development and femtoscopy.
会場: セミナー室 (359号室) 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Generative Models for Statistical Field Theories
2025年6月25日(水) 15:00 - 16:00
王 凌霄 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
In the final talk of the DEEP-IN series, we will explore the role of generative models in learning phase transitions and sampling in lattice systems. First, we demonstrate how generative models can serve as global samplers by learning the underlying probability distributions. This enables the sampling of configurations more efficiently for lattice field theories. We will also demonstrate how the ferromagnetic phase transition, the Kosterlitz-Thouless transition, and quantum phase transitions can be identified from generative models. I will briefly introduce generative diffusion models, which can be interpreted as a stochastic quantization scheme. This opens a new path for understanding deep generative models. This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.
会場: 理化学研究所 和光キャンパス 研究本館3階 345-347 (メイン会場) / via Zoom
イベント公式言語: 英語
36 イベント