3 papers
cs.LG2026
Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems
Yuxin Guo, Dongrui Deng, Pulkit Grover
Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and scientific discovery, where t…
quant-ph2025
Entanglement-induced provable and robust quantum learning advantages
Haimeng Zhao, Dong-Ling Deng
Quantum computing holds unparalleled potentials to enhance machine learning. However, a demonstration of quantum learning advantage has not been achieved so far. We make a step for…
quant-ph2024
Classical Verification of Quantum Learning Advantages with Noises
Yinghao Ma, Jiaxi Su, Dong-Ling Deng
Classical verification of quantum learning allows classical clients to reliably leverage quantum computing advantages by interacting with untrusted quantum servers. Yet, current qu…