3 citations · 12 across the 12 of their papers we have counts for
9 papers · 1 filter
DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems
Seongmin Kim, Abhinav Rijal, Yuri Alexeev +5
While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.…
Diagonal-Budgeted Trotterization for Efficient Quantum Hamiltonian Simulation
Srikar Chundury, Blake Burgstahler, Jiajia Li +2
Efficient classical simulation of quantum Hamiltonian dynamics is often bottlenecked by exponential state growth and the overhead of generic sparse linear algebra. We introduce dia…
Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions
Seongmin Kim, Vincent R. Pascuzzi, Travis S. Humble +5
Many real-world problems are naturally formulated as higher-order optimization (HUBO) tasks involving dense, multi-variable interactions, which are challenging to solve with classi…
Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
Seongmin Kim, In-Saeng Suh, Travis S. Humble +3
Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods h…
Quantum solver for single-impurity Anderson models with particle-hole symmetry
Mariia Karabin, Tanvir Sohail, Dmytro Bykov +8
Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottlen…
Scaling Hybrid Quantum-HPC Applications with the Quantum Framework
Srikar Chundury, Amir Shehata, Seongmin Kim +6
Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NI…
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…
Advancing Scientific Discovery and Complex Optimization through Distributed Quantum Neural Networks
Seongmin Kim, In-Saeng Suh
Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive r…
Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems
Seongmin Kim, In-Saeng Suh
Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulati…