4 papers
Millikelvin digital-to-analog converter for superconducting quantum processors
Ruizi Hu, Zongyuan Li, Zhancheng Yao +19
Scaling superconducting quantum processors is increasingly constrained by the wiring, heat load, and calibration overhead associated with delivering high-resolution analog signals…
LoPRo: Enhancing Low-Rank Quantization via Permuted Block-Wise Rotation
Hongyaoxing Gu, Lijuan Hu, Liye Yu +2
Post-training quantization (PTQ) enables effective model compression while preserving relatively high accuracy. Current weight-only PTQ methods primarily focus on the challenging s…
FLRQ: Faster LLM Quantization with Flexible Low-Rank Matrix Sketching
Hongyaoxing Gul, Lijuan Hu, Shuzi Niu +1
Traditional post-training quantization (PTQ) is considered an effective approach to reduce model size and accelerate inference of large-scale language models (LLMs). However, exist…
Identify and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Hao Deng, Huijuan Zhan, Lijuan Hu +12
Pinpointing the dissipation mechanisms and evaluating their impacts to the performance of Josephson junction (JJ) are crucial for its application in superconducting circuits. In th…