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Digest - IEEE Antennas and Propagation Society. International Symposium

Quantum Machine Learning for Optimal Antenna Placement

Masoud Salmani Arani, Reza Shahidi, Lihong Zhang

Published Jul 13, 2025• 0 citations
Publisher

Abstract

This work presents a novel framework combining quantum computing and machine learning to optimize base antenna station placement in$5 \mathrm{G} / 6 \mathrm{G}$networks. Initially, spectral clustering is employed to identify dense population areas, providing initial antenna locations. The problem is then reformulated as a Maximum Independent Set (MIS) problem in graph theory to address overlapping coverage constraints. Neutral atom representations of the potential antenna sites are constructed, and the Rydberg Blockade property is utilized to optimize spatial configurations. Using the QuTiP quantum simulator, this method effectively solves the MIS problem, achieving 85% household coverage with the minimum possible number of antenna stations.

DOI
10.1109/ap-s/cnc-usnc-ursi55537.2025.11266785

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This work presents a novel framework combining quantum computing and machine learning to optimize base antenna station placement in5G/6G5 \mathrm{G} / 6 \mathrm{G}5G/6Gnetworks. Initially, spectral clustering is employed to identify dense population areas, providing initial antenna locations. The problem is then reformulated as a Maximum Independent Set (MIS) problem in graph theory to address overlapping coverage constraints. Neutral atom representations of the potential antenna sites are constructed, and the Rydberg Blockade property is utilized to optimize spatial configurations. Using the QuTiP quantum simulator, this method effectively solves the MIS problem, achieving 85% household coverage with the minimum possible number of antenna stations.
In wireless communication networks, many resource optimization problems are nondeterministic polynomial-time hard (NP-hard) due to their combinatorial nature and high computational complexity. Recently, neutral-atom-based quantum computing has emerged as a promising platform for efficiently solving such problems by leveraging quantum superposition and entanglement. However, its application to wireless communication optimization problems remains largely unexplored. In this paper, we investigate the use of neutral-atom quantum platforms to solve the maximum access problem (MAP), formulated as a mixed-integer programming task that jointly considers admission control, user clustering, channel assignment, and power allocation in a non-orthogonal multiple access (NOMA)-enabled uplink network. To reduce the computational burden, the MAP is equivalently reformulated as a maximum independent set (MIS) problem in graph theory. This reformulation enables the use of the neutral atom platform based on Rydberg atom arrays, where the MIS problem is naturally encoded into the physical geometry and blockade constraints of the quantum system. Numerical results demonstrate the feasibility and potential of this approach for addressing large-scale wireless resource optimization problems.
Quantum optimization is expected to resolve many computationally hard problems, such as Radio Resource Allocation (RRA) in wireless communications and networking. However, given noisy intermediate-scale quantum (NISQ) computers that supply a very limited number of effective logical qubits after quantum error correction, computationally efficient algorithms are highly desirable to facilitate quantum advantages, particularly the required number of qubits. To achieve quantum advantageous optimization by small number of qubits, we formulate RRA as a Maximum Independent Set (MIS) on dense conflict graphs, which is challenging for the Quantum Approximate Optimization Algorithm (QAOA) using NISQ computing machines. We further propose a modified Quantum Local Search (QLS) framework that decomposes the global MIS into overlapping subgraphs defined by a tunable neighborhood radius. Hard (feasibility) constraints are enforced via partial mixers, while soft (adjacency) constraints are resolved in a classical post-processing step. Considering practical interference issues that are typically ignored in quantum computational research, we uniquely extend the method to accommodate minimal-adjacency scenarios by specifying shared boundaries causing limited interference. While complexity analysis shows polynomial scaling in the number of feasible placements, computational experiments demonstrate substantial savings on required qubits and simultaneously achieving optimal solutions across multiple adjacency configurations. Efficient quantum optimization of RRA by NISQ computers of significantly fewer qubits is therefore accomplished.
We describe and analyze an architecture for quantum optimization to solve maximum independent set (MIS) problems using neutral atom arrays trapped in optical tweezers. Optimizing independent sets is one of the paradigmatic, NP-hard problems in computer science. Our approach is based on coherent manipulation of atom arrays via the excitation into Rydberg atomic states. Specifically, we show that solutions of MIS problems can be efficiently encoded in the ground state of interacting atoms in 2D arrays by utilizing the Rydberg blockade mechanism. By studying the performance of leading classical algorithms, we identify parameter regimes, where computationally hard instances can be tested using near-term experimental systems. Practical implementations of both quantum annealing and variational quantum optimization algorithms beyond the adiabatic principle are discussed.
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.
Operators deploy large-scale array antennas on 5G base stations and set antenna weights to achieve beams with different angles and widths, enhancing 5G network coverage. Traditional antenna weight optimization is based on the user distribution characteristics of the network. Based on the user measurement reports, minimum drive tests, and engineering parameters, the antenna weight optimization determines the relative positions between hotspot areas with centralized user distribution and base station antennas. Then it calculates the antenna weights in the reverse direction. This method can achieve ideal results in optimizing single-cell antenna weights, but it is unsuitable for simultaneous optimization of multiple cells. Information technology is developing rapidly. 5G communication is evolving towards 6G communication, and the growth of network and business scale brings huge challenges to network optimization. Quantum computing is a new computing model that follows the laws of quantum mechanics. It has a computing speed advantage over classical computing and is expected to become a solution to communication network optimization. This paper presents an antenna weight optimization method based on the filter variational quantum matrix solver algorithm. The method utilizes the powerful solution space representation ability of qubits, and it is helpful to obtain better antenna weight optimization results or accelerate the optimization process.
We study the problem of emergency operation center placement in disaster response, where a minimal number of hubs must be selected to ensure timely coverage of all affected locations. This task can be formulated as a minimum dominating set problem on a graph encoding reachability within a target response time. We propose a hybrid quantum-classical approximation framework that leverages neutral-atom quantum computers as independent set samplers. Candidate dominating sets are constructed from both small maximal independent sets and complements of large independent sets, and are subsequently refined via a lightweight classical procedure. We benchmark the approach on synthetic instances and realistic case studies, and implement it on the Fresnel quantum processor by Pasqal, solving instances of up to 100 nodes. Our results show that quantum-generated samples, despite hardware noise, enable near-optimal solutions of the placement problem. Overall, our results demonstrate that neutral-atom devices operating in analog mode can already be used to tackle graph optimization problems for real-world applications.