Work

Solving larger maximum clique problems using parallel quantum annealing

Year: 2023

Type: article

Abstract: Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be expressed as quadratic unconstrained binary optimization problems. However, the hardware of the quantum annealer manufactured by D-Wave Systems, which we consider in this work, is sparsely connected and moderately sized (on the order of thousands of qubits), thus necessitating a minor-embedding of a logical problem onto the physical qubit hardware. The combination of relatively small hardware sizes and the necessity of a minor-embedding can mean that solving large optimization problems is not possible on current quantum annealers. In this research, we show that a hybrid approach combining parallel quantum annealing with graph decomposition allows one to solve larger optimization problem accurately. We apply the approach to the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.

Source: Quantum Information Processing

DOI: 10.1007/s11128-023-03962-x

Authors: Elijah Pelofske, Georg Hahn, Hristo N. Djidjev

Institutions: Los Alamos National Laboratory, Harvard University, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,690