1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.DC2025★ 1 cited
GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Zhihao Xu, Srikar Chundury, Seongmin Kim +6
Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm…
cs.CE2025
Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
Seongmin Kim, In-Saeng Suh
The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms effici…
quant-ph2025★ 1 cited
Quantum Annealing for Combinatorial Optimization: A Benchmarking Study
Seongmin Kim, Sang-Woo Ahn, In-Saeng Suh +3
Quantum annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical o…