Recent Advances for Quantum Neural Networks in Generative Learning

no code implementations • 7 Jun 2022 • Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, DaCheng Tao

Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.

BIG-bench Machine Learning Quantum Machine Learning

Assessing Graph-based Deep Learning Models for Predicting Flash Point

no code implementations • 26 Feb 2020 • Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan

To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules.

High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS

no code implementations • 16 Sep 2019 • Shihui Yin, Xiaoyu Sun, Shimeng Yu, Jae-sun Seo

In this work, we demonstrate a scalable RRAM based in-memory computing design, termed XNOR-RRAM, which is fabricated in a 90nm CMOS technology with monolithic integration of RRAM devices between metal 1 and 2.

Edge-computing