5 papers
Generative Krylov Subspace Representations for Scalable Quantum Eigensolvers
Changwon Lee, Daniel K. Park
Predicting ground state energies of quantum many-body systems is one of the central computational challenges in quantum chemistry, physics, and materials science. Krylov subspace m…
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,…
Measurement-based Dynamical Decoupling for Fidelity Preservation on Large-scale Quantum Processors
Jeongwoo Jae, Changwon Lee, Juzar Thingna +2
Dynamical decoupling (DD) is a key technique for suppressing decoherence and preserving the performance of quantum algorithms. We introduce a measurement-based DD (MDD) protocol th…
Re-uploading quantum data: a universal function approximator for quantum inputs
Hyunho Cha, Daniel K. Park, Jungwoo Lee
Quantum data re-uploading has proved powerful for classical inputs, where repeatedly encoding features into a small circuit yields universal function approximation. Extending this…
Hamiltonian formulations of centroid-based clustering
Myeonghwan Seong, Daniel K. Park
Clustering is a fundamental task in data science that aims to group data based on their similarities. However, defining similarity is often ambiguous, making it challenging to dete…