期刊
QUANTUM INFORMATION PROCESSING
卷 22, 期 5, 页码 -出版社
SPRINGER
DOI: 10.1007/s11128-023-03962-x
关键词
Quantum annealing; Parallel quantum annealing; Maximum clique; Graph decomposition; Quantum computing; branch and bound; D-wave
Quantum annealing has the potential to solve NP-hard problems, but the current hardware is limited in size, which hinders solving larger optimization problems. In this research, we demonstrate that a hybrid approach combining parallel quantum annealing with graph decomposition can accurately solve the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据