To address the growing multi-user interference in dense wireless networks, we propose an interference-aware Deep Quantum Neural Network (DQNN) for channel estimation in the Non-orthogonal multiple access (NOMA) systems. The proposed method incorporates a hybrid classical-quantum architecture. A Transformer-encoder processes the pilot signals to extract spatiotemporal features. A parameterized quantum circuit maps the processed features into a high-dimensional Hilbert space. The enhancement hinges on an Adaptive Energy Valley Optimization (AEVO) algorithm, which modifies the optimization trajectory using interference-aware preconditioners derived from the interference covariance structure. With the aid of these preconditioners, the DQNN can steer through the NOMA's non-convex terrain characterized by interference to enhance estimation performance. Moreover, interference-aware preconditioning is achieved through a lightweight neural network which adapts to time-varying interference. The successive interference cancellation decoder uses the estimated channel matrix to recover symbols. By further analysing the results, it is noticed that the quantum-enhanced machine learning delivers better results than the classical ones. The proposed framework enhances the state-of-the-art in NOMA channel estimation, while also providing a general framework for interference-aware optimization in quantum machine learning. At 10 dB SNR, the AEVO-DQNN method with a 16x16 antenna array obtained a minimum NMSE of 0.012288 and a minimum BER of 0.013023. Further, the proposed method outperforms the competing methods in terms of NMSE/BER mean with 95% confidence intervals, interference rejection ratio analysis, sensitivity to estimation error and estimated interference covariance, and paired t-test analysis.
To address the growing multi-user interference in dense wireless networks, we propose an interference-aware Deep Quantum Neural Network (DQNN) for channel estimation in the Non-orthogonal multiple access (NOMA) systems. The proposed method incorporates a hybrid classical-quantum architecture. A Transformer-encoder processes the pilot signals to extract spatiotemporal features. A parameterized quantum circuit maps the processed features into a high-dimensional Hilbert space. The enhancement hinges on an Adaptive Energy Valley Optimization (AEVO) algorithm, which modifies the optimization trajectory using interference-aware preconditioners derived from the interference covariance structure. With the aid of these preconditioners, the DQNN can steer through the NOMA's non-convex terrain characterized by interference to enhance estimation performance. Moreover, interference-aware preconditioning is achieved through a lightweight neural network which adapts to time-varying interference. The successive interference cancellation decoder uses the estimated channel matrix to recover symbols. By further analysing the results, it is noticed that the quantum-enhanced machine learning delivers better results than the classical ones. The proposed framework enhances the state-of-the-art in NOMA channel estimation, while also providing a general framework for interference-aware optimization in quantum machine learning. At 10 dB SNR, the AEVO-DQNN method with a 16x16 antenna array obtained a minimum NMSE of 0.012288 and a minimum BER of 0.013023. Further, the proposed method outperforms the competing methods in terms of NMSE/BER mean with 95% confidence intervals, interference rejection ratio analysis, sensitivity to estimation error and estimated interference covariance, and paired t-test analysis.
Accurate channel estimation is a fundamental requirement for high-performance wireless communication systems, where conventional deep learning approaches often suffer from excessive trainable parameters and high memory cost as antenna and subcarrier dimensions scale up. This paper proposes a quantum machine learning (QML)-based channel estimation framework that addresses this scalability challenge through a compact hybrid quantum-classical architecture. An 8-qubit quantum neural network (QNN) based on a parameterized quantum circuit (PQC) encodes received pilot-domain features using both Cartesian and polar components through Hadamard initialization and rotation-based quantum encoding, and learns channel representations using trainable variational rotations and controlled-Z (CZ) entanglement. The Pauli-Z measurement outputs are then processed by a lightweight classical dense head to produce the final channel estimate. The proposed QNN is compared with a classical deep neural network (DNN) and a QNN without hidden dense layers over a 2 × 1 Rayleigh fading multiple-input single-output (MISO) channel across an SNR range of 10 dB to 30 dB. Simulation results show that the proposed hybrid QNN achieves the lowest normalized mean square error (NMSE) across the entire evaluated SNR range while using only 1,012 trainable parameters, compared with 3,356 for the DNN. At 30 dB SNR, the proposed QNN achieves the lowest NMSE of approximately − 20.24 dB, compared with approximately − 17.54 dB for the DNN baseline. An ablation study further confirms that the dense post-processing head is essential for accurate regression from raw quantum measurement outputs. These results suggest that compact hybrid quantum-classical estimators offer a promising direction for accurate and parameter-efficient channel estimation in next-generation wireless communication systems.
Abstract This study presents a novel approach combining deep learning with quantum computing for channel estimation and interference management in multi-user MIMO systems. Addressing the limitations of traditional channel estimation methods in complex wireless communication environments, we introduce quantum computing techniques, including quantum Fourier transforms and quantum state reconstruction algorithms, to enhance the accuracy and efficiency of channel estimation. Building on the results from quantum computing, we further optimize the outcomes using deep learning networks, focusing specifically on interference cancellation and data compression. In the experimental setup, we simulated the channel matrix of a multi-user MIMO system on a quantum computing platform and trained a convolutional neural network (CNN) for deep learning optimization. The findings demonstrate that the integration of quantum computing and deep learning substantially enhances system performance, particularly in managing interference and processing data in complex channel environments. This study not only provides new technical methods for channel estimation and interference management in multi-user communication systems but also paves the way for further research into the combined application of quantum computing and deep learning.
Sixth-generation (6G) wireless networks are expected to deliver transformative capabilities including ultra-reliable low-latency communications and massive connectivity. Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology by dynamically manipulating wireless propagation through programmable reflecting elements. However, accurate channel estimation in RIS-assisted networks faces significant challenges from cascaded channel structures, passive elements, and high-dimensional parameter spaces. Quantum machine learning (QML) offers a fundamentally different computational paradigm that may address these challenges through quantum properties. This article provides a tutorial overview of QML for channel estimation in RIS-assisted 6G networks. We present RIS-assisted system architectures and channel estimation challenges, introduce quantum computing fundamentals, and survey QML architectures. As a use case, we consider a hybrid quantum-classical framework, e.g., convolutional neural network (CNN)-quantum long short-term memory (QLSTM), for channel estimation in RIS-assisted non-orthogonal multiple access (NOMA) systems. This case study shows consistent accuracy improvements compared to quantum and classical benchmarks. Finally, we discuss implementation challenges and future research directions for practical deployment of quantum-enhanced wireless systems.
This paper investigates advanced signal processing techniques to address the critical challenge of Multi-User Interference (MUI) within the demanding framework of 6th Generation (6G) wireless systems. The study presents a direct comparative analysis between classical deep learning and emerging quantum machine learning paradigms. We first establish the capabilities of Convolutional Neural Networks (CNNs), a state-of-the-art deep learning model, in handling the complex, non-ideal channel environments of modern wireless communications. Subsequently, we explore the theoretical advantages and practical performance of Quantum Convolutional Neural Networks (QCNNs), positing them as a next-generation solution for processing unprecedented data complexity. Through rigorous simulation in a multi-user SIMO uplink scenario, this paper demonstrates that QCNN consistently outperforms its classical CNN counterpart in both signal detection accuracy and overall system sum rate, particularly in low signal-to-noise ratio (SNR) regimes, all while utilizing significantly fewer model parameters. The core contribution of this work is to provide direct empirical evidence of the potential quantum advantage offered by QCNNs for the complex signal processing tasks inherent to future 6G systems.
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.
This paper proposes a quantum neural network (QNN) to address joint optimization in wireless communication. By utilizing the advantages of quantum entanglement and superposition in quantum computing and machine learning, QNN can solve optimization problems with lower complexity than classical neural networks, due to its parallel processing capabilities. Specifically, this study applies QNN to jointly optimize digital precoder-combiner and power allocation in a multi-user multiple-input multiple-output non-orthogonal multiple access (MU MIMO-NOMA) system. The performance of the QNN is analyzed and presented.