Massive MIMO beamforming for 6G networks faces a fundamental tradeoff between solution quality and computational complexity. Exhaustive search guarantees optimal antenna selection; however, this becomes prohibitively expensive for arrays exceeding 16 elements, while polynomial-time classical heuristics sacrifice 15–25% performance to achieve practical scalability. This paper introduces a quantum-enhanced optimization framework using the Quantum Approximate Optimization Algorithm (QAOA) to address this challenge for IoT-integrated 6G massive MIMO systems. Our approach combines quantum solution exploration with classical parameter optimization, integrating realistic 3GPP TR 38.901 channel models across 28–60 GHz bands and heterogeneous IoT device characteristics (mMTC, URLLC, eMBB). The framework incorporates an adaptive penalty mechanism that achieves constraint satisfaction within five iterations while maintaining polynomial complexity. Statistical validation across 50 independent channel realizations demonstrates significant advantages: 10–20% spectral efficiency improvement over classical heuristics (p<0.001, Cohen’s d=1.24), 35–42% IoT energy reduction, and 90–95% near-optimal solution quality compared to 65–85% for polynomial-time classical methods. Hardware validation on IBM quantum platforms (127–133 qubits) confirms practical feasibility for medium-scale systems with M≤16 antennas, achieving 89.3% of ideal performance with 22% measurement success rate. Current hardware limitations restrict deployment to proof-of-concept demonstrations, with full-scale 6G implementations requiring quantum error correction projected for 2030+.
Massive MIMO beamforming for 6G networks faces a fundamental tradeoff between solution quality and computational complexity. Exhaustive search guarantees optimal antenna selection; however, this becomes prohibitively expensive for arrays exceeding 16 elements, while polynomial-time classical heuristics sacrifice 15–25% performance to achieve practical scalability. This paper introduces a quantum-enhanced optimization framework using the Quantum Approximate Optimization Algorithm (QAOA) to address this challenge for IoT-integrated 6G massive MIMO systems. Our approach combines quantum solution exploration with classical parameter optimization, integrating realistic 3GPP TR 38.901 channel models across 28–60 GHz bands and heterogeneous IoT device characteristics (mMTC, URLLC, eMBB). The framework incorporates an adaptive penalty mechanism that achieves constraint satisfaction within five iterations while maintaining polynomial complexity. Statistical validation across 50 independent channel realizations demonstrates significant advantages: 10–20% spectral efficiency improvement over classical heuristics (p<0.001, Cohen’s d=1.24), 35–42% IoT energy reduction, and 90–95% near-optimal solution quality compared to 65–85% for polynomial-time classical methods. Hardware validation on IBM quantum platforms (127–133 qubits) confirms practical feasibility for medium-scale systems with M≤16 antennas, achieving 89.3% of ideal performance with 22% measurement success rate. Current hardware limitations restrict deployment to proof-of-concept demonstrations, with full-scale 6G implementations requiring quantum error correction projected for 2030+.
Beamforming optimization in 6G networks is essential for ensuring energy-efficient and interference-aware transmission, where ultra-reliable low-latency communication (URLLC) and scalable antenna technologies play a key role. Classical optimization methods face scalability challenges, making real-time beamforming infeasible. This paper applies the quantum approximate optimization algorithm (QAOA) to minimize transmit power while ensuring signal quality constraints. The problem is formulated as a quadratic unconstrained binary optimization (QUBO) and solved using quantum simulation. Simulation results demonstrate that QAOA achieves lower transmit power compared to classical solvers, with faster convergence and improved efficiency. These findings suggest that quantum computing can significantly enhance beamforming optimization, paving the way for its integration into future wireless networks.
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.
Multiple-input multiple-output (MIMO) is critical for 6G communication, offering improved spectral efficiency and reliability. However, conventional fully digital designs face significant challenges due to high hardware complexity and power consumption. Low-bit MIMO architectures, such as those employingb-bit quantized phase shifters, provide a cost-effective alternative but introduce NP-hard combinatorial problems in the pre- and post-coding design. This paper explores the use of the Quantum Approximate Optimization Algorithm (QAOA) and alternating optimization to address the problem ofb-bit quantized phase shifters both at the transmitter and the receiver. We demonstrate that the structure of this quantized beamforming problem aligns naturally with hybrid-classical methods like QAOA, as the phase shifts used in beamforming can be directly mapped to rotation gates in a quantum circuit. Notably, this paper is the first to show that theoretical connection. Then, the Hamiltonian derivation analysis for theb-bit case is presented, which could have applications in different fields, such as integrated sensing and communication, and emerging quantum algorithms such as quantum machine learning. In addition, a warm-start QAOA approach is studied which improves computational efficiency. Numerical results highlight the effectiveness of the proposed methods in achieving an improved quantized beamforming gain over their classical optimization benchmarks from the literature.
As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.
Channel estimation is one of the most fundamental and performance-limiting operations in fifth-generation (5G) and emerging sixth-generation (6G) wireless networks, where ultra-massive MIMO arrays, millimetre-wave and terahertz carrier frequencies, and non-Gaussian hardware-induced noise combine to render classical least-squares and minimum mean squared error estimators inadequate. Deep learning has narrowed the performance gap, yet it offers no information-theoretic guarantee on how close any particular network architecture is to the fundamental noise floor. This paper introduces the AI-Quantum Channel Modeling and Estimation (AQCME) framework, which integrates variational quantum circuits, quantum signal processing, and a quantum eigenspace denoising operator into a unified trainable architecture for simultaneous channel estimation and noise suppression in MIMO-OFDM systems. The defining theoretical result is a quantum Cramer-Rao lower bound on normalised mean squared error, grounded in the quantum Fisher information matrix of the encoded channel state, together with a rigorous proof that the AQCME estimator approaches this bound as training data grows – to the best of our knowledge the first constructive achievability result of its kind in the wireless channel estimation literature. The framework is supported by formal analysis of quantum channel state preparation, quantum kernel universality with an explicit barren-plateau trainability discussion, ergodic spectral efficiency, Nakagami-m outage probability, and convergence of the hybrid quantum-classical gradient descent. Monte Carlo simulations over 50,000 channel realisations, reported with 90 percent confidence intervals throughout, validate every theoretical prediction. Ablation studies across circuit depths and qubit counts, a hardware-noise sweep over gate error rates from 1e-4 to 1e-2, and hyperparameter sensitivity analysis confirm robustness. At 20 dB signal-to-noise ratio, AQCME reduces normalised mean squared error by 4.8 dB over MMSE and by 2.1 dB over the best deep learning baseline; under emulated near-term noisy quantum hardware conditions it still outperforms MMSE by 2.6 dB. All quantum results in this work are obtained by exact classical statevector emulation of the variational circuit; no physical quantum processor was used, and reported hardware-noise behaviour is modelled by a depolarising channel. Spectral efficiency improves by up to 22.4 percent in massive MIMO scenarios, the URLLC outage target of 1e-5 is met at 6.2 dB lower signal-to-noise ratio than with MMSE, and inference complexity scales logarithmically with antenna count rather than quartically as with MMSE matrix inversion.
With the emergence of sixth-generation (6G) wireless networks, ensuring information-theoretic security at extremely high data rates has become a critical challenge. Conventional cryptographic techniques are increasingly vulnerable to quantum computing attacks, motivating the integration of Quantum Key Distribution (QKD) with advanced wireless technologies. This paper proposes a hybrid QKD–Massive MIMO framework operating in the terahertz (THz) band (10–30 THz) to evaluate secure key generation performance under realistic distance, frequency, and antenna scaling conditions. A security-aware Secret Key Rate (SKR) model is developed by incorporating Quantum Bit Error Rate (QBER) constraints and distance-dependent attenuation. Extensive simulations analyze SKR variation with transmission distance (1–10 m), carrier frequency, and antenna array size (32–1024). Results demonstrate that massive MIMO significantly enhances SKR and extends the secure communication range, while higher THz frequencies introduce stronger attenuation. The proposed framework validates the feasibility of scalable quantum-secure THz communications for future 6G networks.