Year: 2023
Type: conference-paper
Abstract: Combinatorial optimization is one of the fields where near term quantum devices are being utilized with hybrid quantum-classical algorithms to demonstrate potentially practical applications of quantum computing. One of the most well studied problems in combinatorial optimization is the Max-Cut problem. The problem is also highly relevant to quantum and other types of “post Moore” architectures due to its similarity with the Ising model and other reasons. In this paper, we introduce a scalable hybrid multilevel approach to solve large instances of Max-Cut using both classical only solvers and quantum approximate optimization algorithm (QAOA). We compare the results of our solver to existing state of the art large-scale Max-Cut solvers. We demonstrate excellent performance of both classical and hybrid quantum-classical approaches and show that using QAOA within our framework is comparable to classical approaches. Reproducibility: Our solver is publicly available at https://github.com/angone/MLMax-cut.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Defense, U.S. Department of Energy, Defense Advanced Research Projects Agency, Office of Science, Advanced Research Projects Agency, Advanced Scientific Computing Research, Laboratory Directed Research and Development, Argonne National Laboratory
Awards: G102163711, G107546177, G1313983767, G1664757510, G1829968774, G2287611682, G262962599, G3075337988, MRI: Acquisition of a Big Data and High Performance Computing System to Catalyze Delaware Research and Education, G498139845, G513797252, G6848031779, G6918803902, G788322952, G878601127, G969889393, G993149817
OpenAlex ID: W4390188977