3 citations · 12 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
Distributed Quantum Approximate Optimization Algorithm on a Quantum-Centric Supercomputing Architecture
Seongmin Kim, Vincent R. Pascuzzi, Zhihao Xu +3
Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum comp…
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…