In this study, an overloaded large-scale distributed antenna network is considered, for which the number of active users is larger than that of antennas distributed in a base station coverage area (called a cell). To avoid overload, users in each cell are divided into multiple user groups, and, to reduce the computational complexity required for multi-user multiple-input and multiple-output (MU-MIMO), users in each user group are grouped into multiple user clusters so that cluster-wise distributed MU-MIMO can be performed in parallel in each user group. However, as the network size increases, conventional computational methods may not be able to solve combinatorial optimization problems, such as user scheduling and user clustering, which are required for performing cluster-wise distributed MU-MIMO in a finite amount of time. In this study, we apply quantum computing to solve the combinatorial optimization problems of user scheduling and clustering for an overloaded distributed antenna network and propose a quantum computing-based user scheduling and clustering method. The results of computer simulations indicate that as the technology of quantum computers and their related algorithms evolves in the future, the proposed method can realize large-scale dense wireless systems and realize real-time optimization with a short optimization execution cycle.