3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Masato Ohnishi, Tianqi Deng, Pol Torres +16
Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their th…
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…
Quantum-Inspired Genetic Algorithm for Designing Planar Multilayer Photonic Structure
Zhihao Xu, Wenjie Shang, Seongmin Kim +3
Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computi…