This paper focuses on the AUD in a NOMA network. To do so with a reduced complexity, we propose a new strategy based on Grover's quantum algorithm to perform the minimum searching so as to implement the ML receiver. As current quantum processors still suffer from noise in the so-called NISQ era, we propose to use Grover's routine with a reduced number of iterations but with several trials. We show that this approach presents a complexity advantage and allows to reach higher success probabilities than Grover's. This strategy may permit to timely deploy such AUD receiver even without perfect quantum (devices.
This paper focuses on the AUD in a NOMA network. To do so with a reduced complexity, we propose a new strategy based on Grover's quantum algorithm to perform the minimum searching so as to implement the ML receiver. As current quantum processors still suffer from noise in the so-called NISQ era, we propose to use Grover's routine with a reduced number of iterations but with several trials. We show that this approach presents a complexity advantage and allows to reach higher success probabilities than Grover's. This strategy may permit to timely deploy such AUD receiver even without perfect quantum (devices.
The strong growth in the number of connected mobile devices has imposed new challenges in efficiently exploiting the available networks resources. Code Domain Non-Orthogonal Multiple Access (NOMA) technique appears as a tremendous efficient solution. Each device uses its assigned code to simultaneously transmit its data along with the user identifier, without any resource reservation exchange, saving precious wireless resources. However, this requires a receiver capable of blindly detecting the active users, which is highly complex. Driven by the promising superposition property of quantum architecture, the goal of this paper is to adapt and apply the quantum Grover algorithm for Active User Detection (AUD) purpose in the context of NOMA, to alleviate the search complexity. This adapted Grover’s algorithm is compared with the optimal classical Maximum Likelihood (ML) AUD receivers, as well as with the basic classical Conventional Correlation Receiver (CCR). A benchmark on the probability of AUD is assessed as a function of the Signal to Noise Ratio (SNR) of the received signal. We show that our adapted Grover’s algorithm is very promising in high SNR regime.
Sixth-generation (6G) networks must deliver ultra-low latency and near-100 percent reliability to support massive-scale Internet of Things (IoT) deployments and Hyper-Reliable Low-Latency Communications (HRLLC). Grant-free access protocols permit devices to transmit without prior scheduling; nevertheless, this uncoordinated transmission introduces uncertainty at the receiver, necessitating Active User Detection (AUD) to ascertain which devices are active. Quantum search methods—most notably Grover’s algorithm—can accelerate AUD, yet they require knowing the optimal number of iterations, which depends on the (typically unknown and time-varying) number of valid solutions induced by the current activity pattern and channel/noise conditions. To overcome this, we formulate an optimization problem that optimizes the number of Grover iterations to maximize detection accuracy and minimize computational cost without any prior activity information. We then apply a Deep Q-Network (DQN) to learn, via deep reinforcement learning, an adaptive policy for selecting the iteration count. Simulation results verify that the DQN converges to an optimal strategy and outperforms two baseline schemes under varying fading conditions and active-user transmit powers.
Grover search is a renowned quantum search algorithm that leverages quantum superposition to find a marked item with quadratic speedup. However, when implemented on Noisy Intermediate-scale Quantum (NISQ) hardware, the required repeated iterations of the oracle and diffusion operators increase exponentially with the number of qubits, resulting in significant noise accumulation. To address this, we propose a hybrid quantum-classical architecture that replaces quantum iterations with updates from a classical optimizer. This optimizer minimizes the expectation value of an oracle Hamiltonian with respect to a parameterized quantum state representing the target bit string. Our parameterized quantum circuit is much shallower than Grover search circuit, and we found that it outperforms Grover search on noisy simulators and NISQ hardware. When the number of qubits is greater than 5, our approach still maintains usable success probability, while the success probability of Grover search is at the same level as random guessing.
Abstract Quantum search algorithm (also known as Grover's algorithm) lays the foundation for many other quantum algorithms. Although it is very simple, its implementation is limited on noisy intermediate-scale quantum (NISQ) processors. Grover's algorithm was designed without considering the physical resources, such as depth, in the real implementations. Therefore, Grover's algorithm can be improved for NISQ devices. In this paper, we demonstrate how to implement quantum search algorithms better on NISQ devices. We present detailed benchmarks of the five-qubit quantum search algorithm on different quantum processors, including IBMQ, IonQ, and Honeywell quantum devices. We report the highest success probability of the five-qubit search algorithm compared to previous works. Our results show that designing the error-aware quantum search algorithms is possible, which can maximally harness the power of NISQ computers.
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 Hoyer (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).
Sixth-Generation (6G) networks will require massive connectivity, ultra-low latency, and robust security, making reliable Active User Detection (AUD) essential for interference control and physical layer protection. This letter proposes a Grover-based physical layer security (PLS) framework for a code-domain non-orthogonal multiple access (CD-NOMA) network, where the base station employs artificial-noise (AN)-assisted beamforming and identifies the active set via Grover's quantum search algorithm. We consider two threat models: passive eavesdroppers formed by detected inactive users, and active eavesdroppers selected as the top f% most frequent transmitters among detected active users. By aligning beams and AN with the Grover-based AUD output, the proposed scheme enlarges the main-wiretap rate gap and significantly improves the average secrecy rate compared with compressive sensing and classical correlation receiver baselines, while approaching maximum-likelihood detection performance with a quadratic reduction in search complexity. The impact of the information/AN power split, the base station transmit power, and the fraction of highly active users treated as eavesdroppers on secrecy is characterized through numerical simulations, and design insights are extracted for 6G PLS under both passive and active eavesdropping.