3 citations · 7 across the 10 of their papers we have counts for
7 papers · 1 filter
Memory-, Circuit-, and Ansatz-Efficient VQLS for CFD on Hybrid Quantum-HPC Systems
Chao Lu, Muralikrishnan Gopalakrishnan Meena, Eduardo Antonio Coello Perez +2
Fluid dynamics workloads are dominated by repeated solves of large, structured linear systems, motivating the search for quantum acceleration. The Variational Quantum Linear Solver…
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.…
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…
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…