The key features of 5G, such as ultra-reliable low latency (URLLC) and massive machine-type communication (mMTC), are designed to address the need for low latency and the ability to connect a large number of devices in the IoT context. To support these constraints, mobile devices transmit information without previously establishing a connection with the base station (BS). This requires the Base Station to detect in real-time the active users (process known as Active User Detection (AUD)).

With classical processors, one can employ the Maximum Likelihood (ML) method (the optimal detector, but suffers from high complexity and delay), or suboptimal ones (which are simpler, but less reliable). Meanwhile, quantum algorithms, particularly Durr and Høyer (DHA) algorithm, addressing minimum searching problems, can significantly reduce complexity while keeping good performances. However, these algorithms were designed for generic problems, and their initialization and parameterization are blindly done. Nonetheless, we can have access to prior information on the system's behavior. Therefore, in this paper, we aim to adapt and improve these quantum algorithms by using prior knowledge on the system for the AUD problem. We first propose a novel algorithm, the Improved Iterative Minimum Searching Algorithm (IIMSA) where we define more efficiently the parameters. Then, further enhancements of IIMSA are obtained thanks to a better initialization of the algorithms by exploiting classical preprocessing of the received signals with classical Conventional Correlation Receiver (CCR) or Zero Forcing (ZF). The obtained results show that these proposed algorithms operate more efficiently (i.e., less complexity with better accuracy).