Abstract
This paper proposes an energy-efficient cluster head selection method in the wireless ad hoc network by using a hybrid quantum-classical approach. The wireless ad hoc network is divided into several clusters via cluster head selection, and the performance of the network topology depends on the distribution of these clusters. For an energy-efficient network topology, none of the selected cluster heads should be neighbors. In addition, all the selected cluster heads should have high energy-consumption efficiency. Accordingly, an energy-efficient cluster head selection policy can be defined as a maximum weight independent set (MWIS) formulation. The cluster head selection policy formulated with MWIS is solved by using the quantum approximate optimization algorithm (QAOA), which is a hybrid quantum-classical algorithm. The accuracy of the proposed energy-efficient cluster head selection via QAOA is verified via simulations.
Highlights
The present era is a turbulent period of technological advancement towards the noisy intermediate-scale quantum (NISQ) era and the 6G era [1,2]
quantum approximate optimization algorithm (QAOA) is one of the simple quantum algorithms that intuitively express the state with qubit rotation via the quantum gate [16,40]
The greedy algorithm, which is useful for maximum weight independent set (MWIS)-based cluster head selection, is used as a comparison algorithm [26]
Summary
The present era is a turbulent period of technological advancement towards the noisy intermediate-scale quantum (NISQ) era and the 6G era [1,2]. In the field of quantum optimization, in particular, various studies have been conducted, based on technologies such as quantum adiabatic algorithm (QAA), variational quantum eigensolver (VQE), and quantum approximate optimization algorithm (QAOA) [3,4,5]. QAOA application studies have been actively conducted [16,17] Along with these various research attempts, QAOA is expected to be useful as a quantum heuristic optimizer in the near future. QAOA is one of the lightest and most flexible hybrid quantum-classical optimization algorithms in the NISQ era and is suitable for application to various graph-based systems [5,29]. Before discussing a hybrid quantum-classical approach to energy-efficient cluster head selection in limited systems, this section describes the QAOA and a graph-based MWIS formulation
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