时 间:14:30-16:30, Oct 15, 2026 (Thu)地 点:RM S527, MMW Building内容:Circuit depth is a fundamental proxy for computational time. As has long been studied in the classical field, a similar question central in quantum complexity is: Does each additional layer of gates really increase computational power? We answer this question in the affirmative within the quantum complexity class $\math...
时 间:11:00-12:00, Oct 9, 2026 (Fri)地 点:RM S527, MMW Building or Venue: https://caltech.zoom.us/j/87834027070 (password: BB1984)内容:Despite significant progress on quantum low-density parity-check (qLDPC) codes, building qLDPC processors that are high-rate, high-throughput, hardware-friendly, and fast-to-decode remains a challenge. In this work, we introduce mitten codes, a family of ...
时 间:11:00-12:00, Sep 24, 2026 (Thu)地 点:RM 1-202, FIT Building内容:Quantum computers have the potential to solve problems that are intractable for classical computers. However, building a universal, fault-tolerant quantum computer remains a significant technological challenge. Before fully fledged quantum computers become available, a more immediate question is what can be achieved wi...
时 间:16:00-17:00, Sep 16, 2026 (Wed)地 点:RM 1-222, FIT Building内容:We show that every language in PSPACE that is decidable by a Turing machine in time T(n)=n^{O(log n)} admits a doubly efficient interactive proof system: the prover runs in time polynomial in T(n), and the verifier runs in time polynomial in n. This extends the best previously known regime for such proof systems from T...
时 间:10:00-11:00, Sep 14, 2026 (Mon)地 点:RM 1-222, FIT Building内容:This talk introduces BitGC, a computationally efficient construction for rate-one garbling based on Ring-LWE with key-dependent-message security. The garbling consists of an SWHE-encrypted seed and one bit of stitching information per gate. Its computation requires homomorphically expanding the seed using a low-depth P...
时 间:10:00-11:00, Sep 14, 2026 (Mon)地 点:https://meeting.tencent.com/dm/kMlAAN2VkyZ8 #腾讯会议:554-221-000内容:Modern AI infrastructure combines specialized training runtimes, GPU kernels, and communication libraries. Optimizing these components individually, however, does not necessarily meet end-to-end requirements for performance and reliability. My research examines these requirem...