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03-16
2026
Generative Social Choice
演讲人：Ariel Procaccia [Harvard University] 时间：09:45-12:30, Mar 16, 2026 (Mon)地点： Lecture Hall, FIT Building内容：How should one design collective decision-making processes capable of handling enormous sets of alternatives like all possible policies, bills, or statements? I argue that this challenge can be addressed through a framework called generative social choice, which fuses the rig...
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02-28
2026
Scaling Trust without Trusting
演讲人：Binyi Chen时间：14:00-15:00, Feb 28, 2026 (Sat)地点： RM 1-222, FIT Building内容：In an era of AI and digitalization, we face a dual challenge in building trust while preserving privacy. For example, social platforms are flooded with unverifiable AI-generated content, and online services require users to excessively expose their personal data. Zero-Knowledge Succinct Proofs (ZK-SNARKs) ...
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02-27
2026
Benchmarking and fidelity response theory of high-fidelity Rydberg gates
演讲人：孙向恺 [Caltech] 时间：10:00-12:00, Feb 27, 2026 (Fri)地点： Lecture Hall, FIT Building内容：The fidelity of entangling operations is a key figure of merit in quantum information processing, especially in the context of quantum error correction. High-fidelity entangling gates in neutral atom arrays have seen remarkable advancement recently. A full understanding of error sources and thei...
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02-02
2026
Quantum Learning and Simulation with Continuous-Variable Quantum Systems
演讲人：罗迪 [清华大学]时间：14:00-15:30, Feb 2, 2026 (Mon)地点： RM S527, MMW Building内容：Recent advancements in continuous-variable (CV) quantum systems are opening up growing opportunities for applications in both quantum learning and quantum simulation. In this talk, I will first discuss machine-learning approaches to multi-mode quantum tomography, enabling efficient reconstruction and ch...
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01-15
2026
Verifiable quantum advantage with peaked circuits
演讲人：张宇轩时间：10:00-11:30, Jan 15, 2026 (Thu)地点： 蒙民伟科技大楼南楼527会议室内容：Over a decade after its proposal, the idea of using quantum computers to sample hard distributions has remained a key path to demonstrating quantum advantage. Yet a severe drawback remains: the anti-concentrated distributions believed to be hard to classically simulate are typically hard to verify without...
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12-26
2025
Rationality of multi-agent learning and uncertainty quantification for the generative models
演讲人：Yian Ma (马易安) [University of California, San Diego]时间：13:30-15:00, Dec 26, 2025 (Fri)地点：RM 1-222, FIT Building内容：In the first part of the talk, I will discuss an interesting phenomenon in multiagent learning, that the mixed Nash equilibria are uniformly stable if and only if they are collectively rational. This justifies the effusive use of multi-agent learning systems and r...