As the demand for higher data rates and seamless connectivity escalates in the era of 5G communications, the integration of advanced technologies becomes imperative to address the associated challenges. This review paper explores the transformative potential of Quantum-Inspired Deep Learning (QIDL) techniques in enhancing spectral efficiency within Massive Multiple Input, Multiple Output (MIMO) systems for 5G networks. Drawing inspiration from principles derived from quantum computing, QIDL introduces a paradigm shift in how deep learning models process information. By incorporating the principles of superposition and entanglement, QIDL models exhibit a unique capability to simultaneously explore diverse possibilities, thereby optimizing the utilization of spectral resources. This article gives a thorough review of Massive MIMO technology and its function in 5G communications as it stands right now. The article continues by explaining how the complicated and ever-changing wireless communication situations may be overcome by utilizing QIDL, which is a novel technique. Through a systematic analysis of existing literature and research endeavors, the paper highlights the potential benefits and limitations of employing QIDL in Massive MIMO for enhancing spectral efficiency.
As the demand for higher data rates and seamless connectivity escalates in the era of 5G communications, the integration of advanced technologies becomes imperative to address the associated challenges. This review paper explores the transformative potential of Quantum-Inspired Deep Learning (QIDL) techniques in enhancing spectral efficiency within Massive Multiple Input, Multiple Output (MIMO) systems for 5G networks. Drawing inspiration from principles derived from quantum computing, QIDL introduces a paradigm shift in how deep learning models process information. By incorporating the principles of superposition and entanglement, QIDL models exhibit a unique capability to simultaneously explore diverse possibilities, thereby optimizing the utilization of spectral resources. This article gives a thorough review of Massive MIMO technology and its function in 5G communications as it stands right now. The article continues by explaining how the complicated and ever-changing wireless communication situations may be overcome by utilizing QIDL, which is a novel technique. Through a systematic analysis of existing literature and research endeavors, the paper highlights the potential benefits and limitations of employing QIDL in Massive MIMO for enhancing spectral efficiency.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNN) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling.
This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.
While 5G is being deployed worldwide, 6G is receiving increasing attention from researchers to meet the growing demand for higher data rates, lower latency, higher density, and seamless communications worldwide. To meet the stringent requirements of 6G wireless communications networks, AI-integrated communications have become an indispensable part of supporting 6G systems with intelligence, automation, and big data training capabilities. However, traditional artificial intelligence (AI) systems are difficult to meet the stringent latency and high throughput requirements of 6G with limited resources. In this article, we summarize, analyze, discuss the potential, and benefits of Quantum Reinforcement Learning (QRL) in 6G. As an example, we show the superiority of QRL in dynamic spectrum access compared to the conventional Deep Reinforcement Learning (DRL) approach. In addition, we provide an overview of what DRL has accomplished in 6G and its challenges and limitations. From there, we introduce QRL and potential research directions that should continue to be of interest in 6G. To the best of our knowledge, this is the first review and vision article on QRL for 6G wireless communication networks.
The forthcoming sixth generation (6G) of wireless networks requires fundamental rethinking of network intelligence, driven by the transition toward ubiquitous cognitive networks and the unprecedented complexity beyond 5G. The optimization demands of 6G surpass the capabilities of classical heuristics and conventional Machine Learning (ML), which encounter significant limitations in addressing dimensionality challenges in ultra-massive multiple-input multiple-output/terahertz/reconfigurable intelligent surfaces-assisted systems, stringent sub-millisecond latency requirements, and severe energy bottlenecks at the edge. Motivated by these gaps, this review investigates Quantum Machine Learning (QML) as a transformative solution, merging quantum mechanics with data-driven intelligence. We propose a unified and forward-looking perspective on ML integration for 6G, bridging previously siloed research domains such as cross-layer optimization, semantic- and intent-driven communication, and quantum-inspired acceleration. Furthermore, this work systematically reviews quantum-enhanced optimization methods and analyzes QML’s role as an intelligence anchor, demonstrating its potential to provide context-aware, resilient, and sustainable network control across various layers. Ultimately, the paper outlines pathways for integrating QML to ensure timely, scalable, and secure decision-making in the volatile 6G landscape.
Quantum-Inspired Supervised Learning (QISL) leverages principles from quantum mechanics to enhance classical machine learning models, offering a promising pathway to address computational bottlenecks without the need for quantum hardware. By exploiting the high-dimensional expressibility of tensor network structures and efficient optimization techniques, QISL offers reduced parameter complexity and improved scalability compared to conventional deep learning approaches. In this paper, we investigate the application of QISL to key telecommunication network challenges, specifically focusing on throughput prediction and handover optimization. We propose a hybrid architecture that integrates QISL-based tensor representations with neural networks, employing techniques such as Adaptive Cross Approximation (ACA)-based tensor decomposition for memory efficiency and optimized loss functions for stable convergence. Through extensive numerical simulations, we demonstrate that QISL significantly reduces parameter overhead while maintaining competitive or superior prediction performance, highlighting its potential as a scalable and resource-efficient alternative for 5G and Future 6G communication systems.