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Xiaojun Dong is currently a final-year PhD candidate at the University of California, Riverside, in the Computer Science and Engineering department, advised by Yan Gu and Yihan Sun. His research focuses on designing and engineering efficient parallel...
Xiangyun Ding is currently a third-year PhD student at the University of California, Riverside, under the supervision of Prof. Yan Gu and Prof. Yihan Sun. Previously, he earned his bachelor's degree from Tsinghua University, advised by Prof. Wenjian Yu...
Noa Vaknin is a PhD candidate in the Computer Science Department at the Hebrew University of Jerusalem, where she received her master’s and bachelor’s degrees (with honors). Vaknin’s research focuses on accelerating matrix multiplication based on Strassen...
In this talk, we consider how classical linear codes play a role in quantum error correction. We provide new results on CSS-T and triorthogonal codes that support fault-tolerant quantum computing.
Iterative decoder failures of quantum low density parity check (QLDPC) codes are attributed to substructures in the code’s graph, known as trapping sets, as well as degenerate errors that can arise in quantum codes. Failure inducing sets are subsets of codeword coordinates that, when initially in error, lead to decoding failure in a trapping set. In this talk, we examine failure inducing sets of QLDPC codes under syndrome-based iterative decoding. We show how redundancy can affect the presence of these sets for some families of QLPDC codes. In particular, we provide a graph condition that guarantees no syndrome mismatch, thereby giving insight to how to add redundancy to improve the decoder performance.
Abstract not available.
We consider the problem of recovering a sparse binary vector from generalized linear measurements. Our analysis focuses on the linear estimation algorithm introduced by Plan, Vershynin, and Yudovina (2017), alongside information-theoretic lower bounds on the number of measurements required for recovery. These results imply tight sample complexity results for logistic regression and one bit compressed sensing. We also consider the problem of sparse linear regression, where we give tight sample complexity characterisation using a maximum likelihood estimator.