387 citations · 406 across the 12 of their papers we have counts for
5 papers · 1 filter
Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding
Ju-Young Ryu, Tak Hur, Daniel K. Park
Understanding generalization is essential for assessing the practical viability of quantum machine learning. In many applications, quantum models operate on classical data that mus…
When Quantum Meets AI: Quantum Methods for Machine Learning and Machine Learning Methods for Quantum Systems
Tak Hur
This thesis studies the intersection of quantum computing and artificial intelligence in two directions: quantum methods for machine learning and machine learning methods for quant…
Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States
Tak Hur
Stochastic reconfiguration (SR) is the standard optimizer for neural quantum states (NQS), but modern NQS often have far more parameters than Monte Carlo samples. We show that in t…
Machine Learning Approaches to Decoding Topological Quantum Codes
Changwon Lee, Tak Hur, Jeongwoo Jae +1
Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve…
Generative Modeling of Quantum Distribution with Functional Flow Matching
Jaehoon Hahm, Tak Hur, Joonseok Lee +1
The emergence of powerful deep generative models based on diffusion and flow matching has enabled the learning and modeling of complex distributions. Learning quantum distributions…