4 papers
Diabatic quantum annealing for training energy-based generative models
Gilhan Kim, Ju-Yeon Gyhm, Daniel K. Park
Energy-based generative models, such as restricted Boltzmann machines (RBMs), require unbiased Boltzmann samples for effective training. Classical Markov chain Monte Carlo methods,…
Gaussian Open Quantum Dynamics and Isomorphism to Superconformal Symmetry
Ju-Yeon Gyhm, Dario Rosa, Dominik Šafránek
Understanding the Lie algebraic structure of a physical problem often makes it easier to find its solution. In this paper, we focus on the Lie algebra of Gaussian-conserving supero…
Fundamental Scaling Limit in Critical Quantum Metrology
Ju-Yeon Gyhm, Hyukjoon Kwon, Myung-Joong Hwang
Critical quantum metrology aims to harness critical properties near quantum phase transitions to enhance parameter estimation precision. However, critical slowing down inherently l…
Boltzmann Sampling by Diabatic Quantum Annealing
Ju-Yeon Gyhm, Gilhan Kim, Hyukjoon Kwon +1
Boltzmann sampling is a central component of many computational frameworks, including numerous algorithms in machine learning. Although quantum annealers have been investigated as…