Seongmin Kim

Memory-, Circuit-, and Ansatz-Efficient VQLS for CFD on Hybrid Quantum-HPC Systems

Fluid dynamics workloads are dominated by repeated solves of large, structured linear systems, motivating the search for quantum acceleration. The Variational Quantum Linear Solver (VQLS) is a leading near-term candidate, but practical deployment on hybrid quantum--high--performance computing (HPC) systems faces three...

S ELE Q TOR : Intelligent Quantum Backend Selection and Runtime Estimation

DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems

DQAOA-GPT is introduced, a hybrid framework that integrates the distributed quantum approximate optimization algorithm (DQAOA), which decomposes a large optimization problem into smaller sub-problems, with GPT-based quantum circuit generation for solving those sub-problems.

Advancing scientific discovery and complex optimization through distributed quantum neural networks

A variational quantum optimization algorithm (VQOA), which employs an ansatz based solely on quantum superposition, where single-qubit rotation gates function analogously to neurons in classical deep neural networks, achieving more than 50 × speedup compared to state-of-the-art optimization algorithms.