Year: 2021
Type: article
Abstract: Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this article, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. To demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave’s quantum annealer and IBM’s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitude larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution. Reproducibility : Our code and data are available at Reference [1].
DOI: 10.1145/3425607
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, Department of Energy, Labor and Economic Growth, Clemson University, Defense Advanced Research Projects Agency, Office of Science, National Nuclear Security Administration, Advanced Research Projects Agency, Argonne National Laboratory, Los Alamos National Laboratory
Awards: G1017065088, G102163711, G107546177, G121915436, G1279347439, G1313983767, Fast and Scalable Multigrid Methods for Hypergraph Partitioning Problems, G1527848044, G1645119126, G1664757510, G1829968774, G1840236740, G1927408643, G203625172, G2287611682, G2411683797, G262962599, G2988778103, G3075337988, G3248584790, G3318903820, MRI: Acquisition of a Cyberinstrument for Interdisciplinary Computational Science and Engineering, G3639612556, G454454283, G498139845, G513797252, G5840121450, G6144542739, G627013438, G6410514744, G6848031779, G6918803902, G70659677, G7354887796, G816254390, G8269158468, G834413596, G8906985441, G969889393, G993149817
OpenAlex ID: W2981590007