Multilevel Combinatorial Optimization across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, limited number of qubits makes it infeasible tackle massive real-world datasets directly near future, leading challenges utilizing these practical purposes. Hybrid quantum-classical algorithms that leverage both and classical types devices are considered as one main strategies apply computing large-scale problems. In this article, we advocate use multilevel frameworks combinatorial optimization a promising general paradigm designing hybrid algorithms. To demonstrate approach, method two well-known problems, namely, Graph Partitioning Problem, Community Detection Problem. We develop solvers with local search on D-Wave’s annealer IBM’s gate-model based processor. carry out experiments graphs orders magnitude larger than current hardware size, observe results comparable state-of-the-art terms quality solution. Reproducibility : Our code data available at Reference [1].