With the advancements in quantum computing, researchers have been focusing on using quantum algorithms to solve combinatorial optimization problems. One of the key models used in this area is the Quadratic Unconstrained Binary Optimization (QUBO) model, which acts as a connection between quantum computers and combinatorial optimization problems. However, there has been a lack of research on QUBO modeling for variant problems related to the Dominating Problem (DP).
The Dominating Problem, also known as the Domination Problem, has applications in various real-world scenarios such as the fire station problem and social network theory. It has several variants, including independent DP, total DP, and k-domination. Despite its importance, there has been a scarcity of quantum computing research on these variant problems.
In this paper, the researchers aim to fill this research gap by investigating QUBO modeling methods for the classic DP and its variants. They propose a QUBO modeling method for the classic DP that can utilize fewer qubits compared to previous studies. This is a significant development as it lowers the barrier for solving DP on quantum computers, making it more accessible and feasible.
Furthermore, the researchers provide QUBO modeling methods for the first time for many variants of DP problems. This expansion of QUBO modeling techniques will contribute to the acceleration of DP’s entry into the quantum era. By providing methods for solving these variant problems on quantum computers, researchers can explore new possibilities and applications in combinatorial optimization.
Overall, this paper contributes to the field of quantum computing by addressing the lack of research on QUBO modeling for variant problems related to the Dominating Problem. The proposed QUBO modeling methods not only optimize the use of qubits for solving the classic DP but also provide new avenues for solving variant problems on quantum computers. This research opens up opportunities for further exploration and advancement in the field of combinatorial optimization on quantum platforms.