3 papers
cs.LG2026
Learning from almost nothing: How neural networks survive heavy input corruption
Justin Tahmassebpur, Asadullah Bhuiyan, Hyejin Kim +1
Learning from imperfect data is a central theme in machine learning, connecting practical questions of robustness to fundamental questions of learnability. Here we examine attribut…
quant-ph2025
Learning measurement-induced phase transitions using attention
Hyejin Kim, Abhishek Kumar, Yiqing Zhou +3
Measurement-induced phase transitions (MIPTs) epitomize new intellectual pursuits inspired by the advent of quantum hardware and the emergence of discrete and programmable circuit…
quant-ph2024
Attention to Quantum Complexity
Hyejin Kim, Yiqing Zhou, Yichen Xu +10
The imminent era of error-corrected quantum computing urgently demands robust methods to characterize complex quantum states, even from limited and noisy measurements. We introduce…