演讲人: Andrea Pizzi [Trinity College, Cambridge] 时间: 10:00-11:00, Sep 17, 2025 (Wed)地点:RM 1-222, FIT Building内容:Chaos makes isolated systems of many interacting particles quickly thermalize and forget their past. While this picture applies to simple observables such as order parameters and correlators, it need not extend to more complex quantities like wavefunction amplitudes, that a...
演讲人: 金缘德 [中国科学院半导体研究所/中国科学院大学]时间: 10:00-11:30, Sep 12, 2025 (Fri)地点:RM S527, MMW Building内容:Quantum phase estimation (QPE) is an important algorithm in quantum information processing, designed to estimate the eigenvalues of a unitary operator, and it plays a significant role in quantum metrology. In our previous work [1], we have established a general framework...
演讲人: 肖艳红 [复旦大学]时间: 16:30-18:00, Sep 11, 2025 (Thu)地点: RM 1-222, FIT Building内容:Physical systems based on coherent atom-light interactions are important for precision measurements and sensing. The measurement sensitivities of classical systems are fundamentally limited by spin projection noise and photon shot noise, hence employing quantum entanglement and squeezed states to...
演讲人: Souradeep Sasmal [University of Electronic Science and Technology of China]时间: 15:00-17:00, Sep 8, 2025 (Mon)地点: RM S327, MMW Building (#腾讯会议:435-802-209)内容:The prevailing consensus is that the sequential sharing of nonlocality in a Bell experiment requires generalised unsharp measurements, since a sharp measurement inevitably destroys the entanglement of the shared state...
演讲人: Sergey Samsonov [HSE University] 时间: 11:00-12:00, Aug 25, 2025 (Mon)地点:11:00-12:00, Aug 25, 2025 (Mon)内容:GFlowNets are a family of generative models that learn to sample objects from a given probability distribution, potentially known only up to a normalizing constant. The key concept behind GFlowNets is the use of two stochastic policies: a forward policy, which incrementally...
演讲人: 程韵 [普林斯顿大学] 时间: 11:00-12:00, Aug 13, 2025 (Wed)地点:RM 1-222, FIT Building (//meeting.tencent.com/dm/hpLuprdKjM45 #腾讯会议:567-960-508)内容:While Vision Language Models (VLMs) are impressive in tasks such as visual question answering (VQA) and image captioning, their ability to apply multi-step reasoning to images has lagged, giving rise to perceptions of modality ...