Quantum communication offers transformative potential for media transmission by addressing the limitations of classical communication systems. To realize this potential, the study proposes a quantum communication framework for transmitting compressed videos over error-prone channels, leveraging quantum superposition. Two channel coding schemes are analyzed: quantum error correction (three-qubit, five-qubit, and seven-qubit codes) and classical error correction (1/3 rate polar code), all operating within the same bandwidth constraints. The proposed systems are benchmarked against a classical communication system using 1/3 rate polar codes. Results show that the three-qubit error correction-based quantum communication system, while simple and efficient, achieves significant performance gains over both classical error correction-based quantum and classical communication systems, with up to 41.42 dB in peak signal-to-noise ratio (PSNR), 0.9639 in structural similarity index measure (SSIM), and 94.4042 in video multimethod assessment fusion (VMAF). However, the five-qubit and seven-qubit systems outperform the three-qubit system, with the seven-qubit system surpassing all others in high noise environments, demonstrating its robustness across various group of pictures (GOP) formats. These findings highlight the trade-offs between simplicity and complexity, as the three-qubit system is practical and efficient, while the five-qubit and seven-qubit channel codes offer higher fidelity and resilience at the cost of increased complexity.

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 $2\times 2$ 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.

Quantum communication systems have the potential to revolutionize media transmission technologies with unparalleled efficiency and reliability. However, practical and scalable implementations are challenged by issues such as channel fading and interference. To address these, we propose a novel approach combining quantum superposition with transmit and receive diversity schemes in Multiple-Input Multiple-Output (MIMO) systems, designed to mitigate the effects of fading for enhanced image transmission. In our simulations, images in JPEG and HEIF formats are channel encoded with rate 1/2 polar coding, converted into qubit superposition states, and transmitted through a 2x2 MIMO system with varied diversity schemes. At the receiver, a quantum decoder reconstructs the classical information, followed by polar decoding to retrieve the original image data. Our approach achieves notable improvements in image quality, with Peak Signal-to-Noise Ratio (PSNR) up to 58.27 dB for JPEG and 64.72 dB for HEIF, and Structural Similarity Index Measure (SSIM) up to 0.9994 for JPEG and 0.9999 for HEIF, outperforming classical systems, especially under low Signal-to-Noise Ratio (SNR) conditions, demonstrating the system’s enhanced ability to maintain image quality in noisy channels. These findings highlight the promise of quantum superposition-based media transmission to set new standards in reliable, high-fidelity communication for next-generation systems.

As wireless networks approach their Shannon capacity limit amid exponentially increasing data demands, semantic communication, which transmits the meaning of data rather than its exact form, has emerged as a compelling alternative. However, a key challenge lies in effectively modeling and mitigating both semantic and technical noise in such systems. We analyze a framework for semantic image transmission that independently and jointly models these two types of noise while employing Turbo codes for robust error detection and correction. We systematically examine their individual and combined impacts through seven experimental scenarios and benchmark the results against state-of-the-art image codecs. Our findings reveal that joint noise modeling not only improves semantic fidelity, but also enhances overall system robustness. Turbo coding proves effective in mitigating both noise types, enabling accurate meaning preservation even under adverse channel conditions. The simulation results demonstrate a reduction of approximately 33% in the channel bit rate when using joint modeling compared to alternative approaches, without compromising visual quality. Comparative evaluations against conventional codecs such as JPEG, JPEG2000, HEIF, and Turbo-coded image transmission systems highlight the superior performance of our semantic-aware strategy in noisy, bandwidth-constrained environments. This research underscores the value of integrating semantic intelligence with classical coding techniques, paving the way for next-generation wireless communication systems that are more efficient, resilient, and customized for emerging applications such as 6G, autonomous systems, and AI-enhanced networks.

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to $50\%$ and in received image quality with an image coding gain of up to 17 dB.

The growing demand for reliable image transmission in noisy vehicular networks exposes the limitations of classical communication, which often suffers from bandwidth constraints and susceptibility to noise. Classical semantic communication addresses this by focusing on essential information, but it still struggles to maintain consistent image quality under adverse channel conditions. To address these limitations, this study proposes a quantum communication system for semantic communication-based image transmission by leveraging quantum superposition. The system first uses a semantic encoder to generate a compact latent representation. This representation is subsequently processed by a channel encoder and mapped into quantum superposition states using a quantum encoder. The resulting quantum states are sent through a noisy quantum channel, where they are measured and decoded at the receiver to recover the corresponding classical bitstream. The bitstream is then channel decoded to recover the latent vector, which is used by a semantic decoder to reconstruct the image. Results from the experiments confirm that the proposed system outperforms classical semantic communication systems, achieving a peak signal-to-noise ratio (PSNR) of up to 28.55 dB, a structural similarity index (SSIM) of up to 0.7996, a universal quality index (UQI) of up to 0.7836, and perfect classification accuracy at channel SNR levels as low as 6 dB, as evaluated by a convolutional neural network (CNN). These results highlight the system's effectiveness in task-oriented communication and show that combining quantum and semantic communication enhances image quality and reliability in challenging vehicular scenarios.