This work presents a novel Generalized Space-Time Block Code (GSTBC) approach, which was inspired by Quantum Error Correction (QEC) to enhance wireless communication performance in hybrid Gaussian–quantum noise environments. The proposed approach integrates space-time fronthaul encoder-decoder compression, where the encoding process was optimized utilizing Singular Value Decomposition (SVD). To achieve the best eigen decomposition, this work employed the Variational Quantum Algorithm (VQA), which leveraged quantum-computing advantages in high-dimensional signal processing. The proposed approach was simulated using a MATLAB-based quantum communication simulator, showing high performance in mitigating noise effects and enhancing signal efficiency. The proposed approach was evaluated considering different user scenarios using the following metrics: Signal-to-Noise Ratio (SNR), Bit Flip Noise Error, and Mean Square Error (MSE). The findings from the simulation highlight the potential of Quantum-Assisted encoding-decoding approaches in future 6G wireless communication systems to provide efficient and reliable data transmission.
This work presents a novel Generalized Space-Time Block Code (GSTBC) approach, which was inspired by Quantum Error Correction (QEC) to enhance wireless communication performance in hybrid Gaussian–quantum noise environments. The proposed approach integrates space-time fronthaul encoder-decoder compression, where the encoding process was optimized utilizing Singular Value Decomposition (SVD). To achieve the best eigen decomposition, this work employed the Variational Quantum Algorithm (VQA), which leveraged quantum-computing advantages in high-dimensional signal processing. The proposed approach was simulated using a MATLAB-based quantum communication simulator, showing high performance in mitigating noise effects and enhancing signal efficiency. The proposed approach was evaluated considering different user scenarios using the following metrics: Signal-to-Noise Ratio (SNR), Bit Flip Noise Error, and Mean Square Error (MSE). The findings from the simulation highlight the potential of Quantum-Assisted encoding-decoding approaches in future 6G wireless communication systems to provide efficient and reliable data transmission.
In upcoming generations of wireless communication (6G), Quantum Information Technology (QIT) will be integrated for high computational speeds and unprecedented security features. Quantum states are very delicate and may be lost or suffer from excessive noise as a result of decoherence during transmission. This especially applies to long-distance quantum links that are required for a global 6G network. The use of Quantum Error Correction (QEC) techniques will be necessary in order to achieve fault-tolerant quantum communication. In this work, the most actionablle QEC and hybrid (quantum-classical) error correction techniques are analyzed, especially with respect to the difficult channel conditions and high Key Performance Indicators (KPI) regarding 6G. In particular, the error suppression, qubit overhead, and decoding time required for various codes (Surface Code, Steane Code, hybrid Reed-Solomon (RS)/Turbo Codes, etc.) will be analyzed in the context of a 6G quantum channel (free-space or noisy fiber) to provide 6G. In the wireless 6G network, the simulation will show that while high-distance QEC codes provide the best ultimate logical error suppression, the enormous resource costs and 6G’s strict latency requirements will lead to the first proposed hybrid classical-QEC.
This research paper explores the application of singular value decomposition (SVD) in quantum image processing (QIP), specifically focusing on the computation of eigenvalues using variational quantum algorithms. SVD is a powerful mathematical tool in image processing, used for tasks such as image compression, noise reduction, and feature extraction. In this study, we propose quantum variational singular value decomposition (QVSVD) based on the variational quantum deflation (VQD) algorithm to determine the eigenvalues that contributes in calculating the singular values of the image matrix and eventually doing the SVD. This facilitates the extraction of eigenvalues with exponential speedup on real hardware compared to classical methods. We detail the implementation of this quantum algorithm within the framework of QIP, highlighting its advantages in terms of computational efficiency and accuracy on a Fault-Tolerant Quantum Computing (FTQC). Furthermore, we present a comparative analysis of the quantum and the classical method of SVD, demonstrating the accuracy of the image data. The simulation results validate the theoretical advantages of using quantum algorithms for SVD in image processing. This work not only underscores the potential of quantum computing in enhancing image processing techniques but also sets the stage for future research in the field, exploring more complex image processing tasks and other quantum algorithms. Our findings suggest that quantum image processing can offer unprecedented capabilities, paving the way for advancements in various applications such as medical imaging, remote sensing, and multimedia processing.
This paper presents a novel quantum error correction protocol leveraging space-time encoding to enhance the resilience of quantum information transmission. Traditional quantum error correction protocols predominantly rely on spatial dimensionality encoding, which often proves insufficient against various noise sources. This protocol introduces a dual-dimensional encoding scheme, integrating both spatial and temporal dimensions, to significantly improve error detection and correction capabilities. The core mechanism utilizes the inherent error-correcting properties of quantum codes, amplified by the redundancy introduced through space-time encoding. The proposed protocol demonstrates a marked improvement in the transmission robustness of quantum information compared to conventional spatial-only approaches. The design focuses on optimizing the encoding and decoding processes to minimize the overhead associated with the space-time scheme while maximizing the error correction performance. Mathematical formulations and key parameters are detailed, providing a comprehensive understanding of the protocol's operational principles and potential performance metrics. This research contributes to the advancement of quantum communication technology by offering a more effective strategy for reliable quantum information transfer.
The rapid growth of next-generation networks has created a strong demand for communication systems that have delivered high reliability, low latency, and resilience under harsh channel conditions. Although classical error correction codes have improved many wireless links, their performance has proved insufficient as data rates increased and channel dynamics became more unpredictable. This study has explored a quantum-inspired error correction framework that has combined structural principles from quantum stabilizer codes with the efficiency of classical block codes. The aim was to provide an adaptive mechanism that has reduced noise effects and supported ultra-reliable communication targets. The problem has emerged from the gap between existing coding techniques and the reliability requirements of mission-critical services. Classical codes have struggled when the channel has exhibited fast fading or burst noise, and quantum codes, while powerful, have required complex hardware. The proposed approach has addressed this gap by adopting quantum-inspired parity structures that have retained the lightweight processing of classical codes while mimicking the robustness observed in quantum systems. The method has employed a hybrid coding model that has integrated a modified stabilizer-like generator with a classical low-density parity check backbone. The encoder has produced redundant qubit-analogous syndromes that have allowed the decoder to infer error patterns with higher confidence. A sequential belief-propagation algorithm has been used, which has adjusted decoding weights according to channel variation. Simulations have been performed over Rayleigh and Rician channels, and the system has been tested under high mobility. The results of the proposed framework demonstrate substantial improvements over conventional coding methods. The hybrid stabilizer-LDPC structure reduces the bit error rate from 22.3% to 0.5% across an SNR range of 0–20 dB and lowers the frame error rate from 45.7% to 1.1%. Throughput improves from 4.2 Mbps at 0 dB to 13.8 Mbps at 20 dB, while the average decoding iterations decrease from 42 to 5, indicating reduced computational complexity. Under high-speed mobility, BER and FER remain low at 2.3% and 4.6%, respectively, while throughput stays above 10.2 Mbps and convergence requires only 12 iterations. These numerical results confirm that the proposed method provides highly reliable, efficient, and adaptive error correction suitable for next-generation networks.
Reliable transmission of high-quality video over wireless channels is challenged by fading and noise, which degrade visual quality and disrupt temporal continuity. To address these issues, this paper proposes a quantum communication framework that integrates quantum superposition with multi-input multi-output (MIMO) spatial diversity techniques to enhance robustness and efficiency in dynamic video transmission. The proposed method converts compressed videos into classical bitstreams, which are then channel-encoded and quantum-encoded into qubit superposition states. These states are transmitted over a 2×2 MIMO system employing varied diversity schemes to mitigate the effects of multipath fading and noise. At the receiver, a quantum decoder reconstructs the classical information, followed by channel decoding to retrieve the video data, and the source decoder reconstructs the final video. Simulation results demonstrate that the quantum MIMO system significantly outperforms equivalent-bandwidth classical MIMO frameworks across diverse signal-to-noise ratio (SNR) conditions, achieving a peak signal-to-noise ratio (PSNR) up to 39.12 dB, structural similarity index (SSIM) up to 0.9471, and video multi-method assessment fusion (VMAF) up to 92.47, with improved error resilience across various group of picture (GOP) formats, highlighting the potential of quantum MIMO communication for enhancing the reliability and quality of video delivery in next-generation wireless networks.
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.