Networks built to sixth-generation (6G) targets will have to hold sub-millisecond latency, push spectral efficiency past what today's massive MIMO deployments achieve, and manage resources across highly heterogeneous nodes with little human supervision. Those targets place a hard demand on the optimizer. Channel estimation, adaptive modulation, beamforming and decoding all involve searching large, non-convex parameter spaces, and the search has to finish inside a coherence interval that may last only a few hundred microseconds. This paper surveys quantum-inspired signal processing (QISP), a family of classical algorithms that borrow structure from quantum computation, including amplitude-weighted superposition sampling, quantum-walk-style exploration, Ising-model formulations and tensor-network compression, but that run entirely on conventional processors. We group the literature under four headings: algorithmic foundations, communication-system architectures, optimization strategies, and implementation constraints. We then propose a reference architecture in which a quantum-inspired optimization engine sits beside a conventional OFDM/MIMO transmit-receive chain and reconfigures modulation, beam-selection and decoding parameters from channel-state feedback. An illustrative simulation grounds the discussion: a representative QISP optimizer is compared against a conventional adaptive baseline over a 4×4 MIMO, QPSK, AWGN-channel OFDM link at signal-to-noise ratios (SNR) from 0 to 20 dB. These numbers demonstrate methodology and are not measurements taken from physical hardware. Within that caveat, the QISP optimizer achieves a lower biterror rate (BER) at low and moderate SNR, and the throughput and latency trends are consistent with the parameter-search efficiency reported in the wider literature. The paper concludes with open problems such as scalability, algorithmic complexity, explainability and standardized benchmarking, and research directions on hybrid quantum-classical processors, reconfigurable intelligent surfaces (RIS), semantic communications, and edge-native QISP inference.
Networks built to sixth-generation (6G) targets will have to hold sub-millisecond latency, push spectral efficiency past what today's massive MIMO deployments achieve, and manage resources across highly heterogeneous nodes with little human supervision. Those targets place a hard demand on the optimizer. Channel estimation, adaptive modulation, beamforming and decoding all involve searching large, non-convex parameter spaces, and the search has to finish inside a coherence interval that may last only a few hundred microseconds. This paper surveys quantum-inspired signal processing (QISP), a family of classical algorithms that borrow structure from quantum computation, including amplitude-weighted superposition sampling, quantum-walk-style exploration, Ising-model formulations and tensor-network compression, but that run entirely on conventional processors. We group the literature under four headings: algorithmic foundations, communication-system architectures, optimization strategies, and implementation constraints. We then propose a reference architecture in which a quantum-inspired optimization engine sits beside a conventional OFDM/MIMO transmit-receive chain and reconfigures modulation, beam-selection and decoding parameters from channel-state feedback. An illustrative simulation grounds the discussion: a representative QISP optimizer is compared against a conventional adaptive baseline over a 4×4 MIMO, QPSK, AWGN-channel OFDM link at signal-to-noise ratios (SNR) from 0 to 20 dB. These numbers demonstrate methodology and are not measurements taken from physical hardware. Within that caveat, the QISP optimizer achieves a lower biterror rate (BER) at low and moderate SNR, and the throughput and latency trends are consistent with the parameter-search efficiency reported in the wider literature. The paper concludes with open problems such as scalability, algorithmic complexity, explainability and standardized benchmarking, and research directions on hybrid quantum-classical processors, reconfigurable intelligent surfaces (RIS), semantic communications, and edge-native QISP inference.
The next-generation wireless sensor networks (WSN) applications related to the 6G communication, smart structural monitoring, and Internet of Things (IoT) require very efficient, low-latency, and energy-efficient signal processing patterns. The current methods of digital signal processing (DSP), although having the capabilities of accomplishing well in terms of resources, fail to achieve the set parameters involving computation and power consumption and resource requirements of autonomous battery-powered sensor nodes that are distributed. In solving this important problem, this project presents a quantum-inspired framework of signal processing (QISP) that implements the key concepts underlying quantum computation, in terms of superposition, amplitude amplification, and entanglement-inspired correlation modeling, into classical embedded systems. The three key algorithmic elements in the proposed QISP framework include the QISP-Denoise, which combines the concepts of amplitude amplification using Grover-like algorithm to clarify the signals; QISP-Transform, which employs the use of sparse-based compressive sensing to reduce the dimensionality of the data, and finally the QISP-Detect which employs quantum walk based filters to detect anomalies and events. They are run on a resource-bounded ARM Cortex-M-style microcontroller simulator, and tested with real biomedical and environmental data sets. The framework brings about impressive gain in the denoising accuracy, compression efficacy, and localization of anomalies with less amount of efficiency, low computation latency and power consumption. This experimentation shows that the QISP framework out buying a ticket online performs traditional DSP and AI-based approaches in terms of signal-to-noise ratio (SNR), mean squared error (MSE), and classification accuracy, all within an energy envelope that can be deployed long-term in a WSN. In addition, QISP is very insensitive to noise and sensing environment variability, which makes it applicable in the brown field fields of dynamic real time sensing like in wearable medical monitoring, smart grids, and structural health monitoring. Bringing the quantum-inspired paradigm into the system in the form of quantum-inspired accelerators is a scalable, affordable strategy to increasing intelligence in embedded edge devices. Such work can be seen as ground zero to the practical application of quantum-classical mixed models to intelligent sensing in resource-limited environments and bring quantum theory and the next generation of wireless sensors together.
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.
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.
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.
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.
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.