3 papers
quant-ph2026
Universal Matrix Multiplication on Quantum Computer
Jiaqi Yao, Tianjian Huang, Zipeng Cai +1
As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inferenc…
quant-ph2025
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing
Zhehui Wang, Benjamin Chen Ming Choong, Tian Huang +4
Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods suc…
cs.LG2024
Optimal Differentially Private Model Training with Public Data
Andrew Lowy, Zeman Li, Tianjian Huang +1
Differential privacy (DP) ensures that training a machine learning model does not leak private data. In practice, we may have access to auxiliary public data that is free of privac…