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R-CCS Cafe
The 283rd R-CCS Cafe (Jan 13, 2026)
The 283rd R-CCS Cafe (Jan 13, 2026)
Japanese| Date | Tue, Jan 13, 2025 |
|---|---|
| Time | 3:00 pm - 4:00 pm (3:00 pm - 3:40 pm two talks, 3:40 pm - 4:00 pm Free discussion) |
| City | Kobe, Japan/Online |
| Place | Lecture Hall (6th floor) at R-CCS, Online seminar on Zoom
|
| Language | Presentation Language: English Presentation Material: English |
| Speakers |
Fukuharu Tanaka Large-Scale Digital Twin Research Team Shaoxiong Li Computational Materials Science Research Team |
Talk Titles and Abstracts
1st Speaker: Fukuharu Tanaka
Title:
Policy Optimization for Pedestrian Traffic Management by Surrogation of Simulation Models
Abstract:
Managing pedestrian traffic at large events requires effective control policies that can be developed and adjusted within a limited time frame. However, conventional approaches rely heavily on repeated multi-agent simulations, which makes policy optimization computationally expensive and difficult to apply in time-sensitive scenarios. In this talk, I will present a method for efficiently optimizing pedestrian traffic management policies by surrogating simulation models with neural networks and applying gradient-based black-box optimization. The proposed approach constructs a differentiable surrogate of a multi-agent simulator, enabling policy parameters to be optimized using gradient information rather than repeated simulator executions. This significantly accelerates the policy development process while maintaining the ability to handle complex, multi-objective evaluation criteria. Our methodology was tested by applying it to actual pedestrian flow data from Koshien Stadium in Hyogo, Japan. The real-world application not only confirmed the practical utility of our optimization technique in effectively managing crowd scenarios but also marked a significant advancement in public safety measures for densely populated areas.
2nd Speaker: Shaoxiong Li
Title:
Quantum Approaches to Financial Tail-Risk Estimation with Correlated Structure
Abstract:
Estimating rare but extreme events is a long-standing challenge in computational science, particularly when such events are driven by correlated fluctuations and cascade mechanisms rather than independent noise. In credit risk modeling, extreme portfolio losses arise from common shocks, heterogeneous responses, and contagion effects, making classical Monte Carlo simulations increasingly inefficient as the dimensionality and dependence increase. In this talk, I will present a quantum-computational framework for tail-risk estimation that builds on Quantum Amplitude Estimation (QAE) while focusing on structural feasibility rather than near-term quantum advantage. The core idea is to embed realistic dependence structures directly into quantum circuits, enabling coherent representation of shocks, default propagation, and loss accumulation. I will discuss how resource-aware quantum arithmetic can be used to aggregate losses, how circuit depth and qubit requirements scale with model complexity, and how preliminary circuit prototypes validate the logical consistency of the approach. The goal is to establish a concrete, analyzable pathway from realistic stochastic models to quantum implementations, and to assess the computational resources required on future quantum devices. More broadly, I will outline how the same framework applies to other rare event problems, positioning quantum computation as a modeling tool for complex financial systems.
Important Notes
- Please turn off your video and microphone when you join the meeting.
- The broadcasting may be interrupted or terminated depending on the network condition or any other unexpected event.
- The program schedule and contents may be modified without prior notice.
- Depending on the utilized device and network environment, it may not be able to watch the session.
- All rights concerning the broadcasted material will belong to the organizer and the presenters, and it is prohibited to copy, modify, or redistribute the total or a part of the broadcasted material without the previous permission of RIKEN.
(Jan 9, 2026)
