Opening

• Myeonghun Park

Unlocking Physics in High Dimensions from Small-Sample Data (chair: Myeonghun Park)

• Aishik Ghosh (Georgia Institute of Technology)

Transformers for collider analyses (chair: Yung-Kyun Noh)

• Ahmed Hammad (KEK, Japan)

Evaluator-guided routing mechanics in modular neural networks (chair: Yung-Kyun Noh)

• Sungjung Kim (SNU)

A physics-informed, global-in-time neural particle method for the spatially homogeneous Landau equation (chair: Aishik Ghosh)

• Yeoneung Kim (SeoulTech)

We propose a physics-informed neural particle method (PINN-PM) for the spatially homogeneous Landau equation. The method adopts a Lagrangian interacting-particle formulation and jointly parameterizes the time-dependent score and the characteristic flow map with neural networks. Instead of advancing particles through explicit time stepping, the Landau dynamics is enforced via a continuous-time residual defined along particle trajectories, yielding a mesh-free solver that can be queried at arbitrary times. We establish a deterministic, global-in-time stability analysis in an $L^2_v$ framework. The deviation between learned and exact characteristics is controlled by three interpretable sources: the score approximation error, the particle approximation error, and the physics residual of the neural flow. This trajectory estimate is then lifted to Wasserstein stability and density reconstruction error via kernel density estimation, resulting in a complete error propagation chain from particle dynamics to macroscopic quantities. At the oracle level, the score error is characterized through the implicit score matching functional via Hyv\"arinen's identity. In practice, the empirical ISM objective provides a computable surrogate for monitoring score accuracy during training. Numerical experiments on analytical benchmarks, including the two- and three-dimensional BKW solutions, as well as reference-free configurations, demonstrate stable transport, preservation of macroscopic invariants, and competitive or improved accuracy compared with time-stepping particle methods while using significantly fewer particles.

Diffusion Models and Thermodynamic Singularity (chair: Aishik Ghosh)

• Yung-Kyun Noh (Hanyang University / KIAS)

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics (chair: Ahmed Hammad)

• Shinjae Yoo (Brookhaven National Laboratory)

Neural Inverse Problems and Degeneracy: SMEFT and Primordial Black Holes (chair: Ahmed Hammad)

• Tae-Geun Kim (Fudan U. / RIKEN)

Distribution-free uncertainty quantification for ML in HEP (chair: Sunghoon Jung)

• Jack Araz

DLScanner and LeStrat-Net: Machine learning for improved Monte Carlo exploration (chair: Sunghoon Jung)

• Raymundo Ramos (Korea Institute for Advanced Study)

Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling (chair: Yeoneung Kim)

• Sangwoong Yoon (Ulsan National Institute of Science and Technology)

Patching solutions with machine learning (chair: Yeoneung Kim)

• Jiheon Lee (KIAS)

Practical and Efficient Verification of Entanglement with Incomplete Measurement Settings (chair: Dong Woo Kang)

• Joonwoo Bae (KAIST)

Neural Causal Information: A Capacity Principle for Query-Separated Representations (chair: Dong Woo Kang)

• Jeongho Bang (Yonsei University)

Tripartite Entanglement in e+e- -> t t Z (chair: Joonwoo Bae)

• Alberto Navarro (Seoultech)

Spin density matrix and Quantum Observables of a two-qubit system at Colliders (chair: Joonwoo Bae)

• Dong Woo Kang (Jeonbuk National University)

Data-Driven Quantum State Tomography: A Diffusion Model Approach for Noisy Quantum Hardware (chair: Jack Araz)

• Changhyoup Lee (Hanyang University)

Quantum Information Meets Collider Physics (chair: Jack Araz)

• Dorival Goncalves (Oklahoma State University)

Exponentially improved quantum simulation of scalar QFT (chair: Myeonghun Park)

• Yingying Li (IHEP)

Digital Quantum Simulation for the Spectroscopy of Schwinger Model (chair: Myeonghun Park)

• Dongwook Ghim (IBS CTPU-PTC)

Leveraging Quantum hardware for Fundamental Physics (chair: Yingying Li)

• Jack Araz (UCL & City St George's, UoL)

Quantum Utility on Noisy Near-Term Quantum Computers (chair: Yingying Li)

• Kwangmin Yu (Brookhaven National Laboratory)

Theory Targets for Dark Matter Experiments: The Case of Magnetic Dipole Dark Matter (chair: Dorival Goncalves)

• Jae Hyeok Chang

Quantum Error Correction for Noise Mitigation in Wave-like Dark Matter Search (chair: Dorival Goncalves)

• Hajime Fukuda (University of Tokyo)

Solid-state defect based quantum sensing : from basics to dark matter detection (chair: Myeonghun Park)

• Paul Junghyun Lee (KIST)

Quantum metrology based on solid-state color centers has advanced significantly through the development of robust sensing protocols, including Ramsey interferometry, Hahn echo, and dynamical decoupling sequences. Color center qubits are particularly promising platforms for quantum sensing due to their ability to operate under a wide range of conditions, from vacuum to ambient environments and from cryogenic to room temperatures, depending on the application. In this talk, I will introduce the fundamental principles of quantum metrology using solid-state color centers, with a particular focus on defect qubits in diamond. I will then discuss recent efforts to achieve quantum-enhanced sensing by exploiting quantum correlations among defect qubits. Finally, I will highlight emerging applications of color center qubits in particle physics, including the search for axion dark matter, and present ongoing experimental efforts in this direction.

Quantum Probes of Axion Dark Matter (chair: Myeonghun Park)

• SungWoo Youn (IBS-DMAG)

Quantum metrology for wavelike dark matter (chair: Hajime Fukuda)

• Bin Xu (Korea Institute for Advanced Study)

Quantum Sensing for High-Frequency Gravitational Wave (chair: Hajime Fukuda)

• Heechan Yi (Yonsei University)

Closing

• Myeonghun Park