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.
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.
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.
Quantum image transmission is a critical enabler for next-generation communication systems, allowing for the reliable exchange of high-quality visual data over error-prone quantum channels. Existing quantum-encoding schemes, however, often suffer from limited efficiency and reduced robustness under noisy conditions. This work introduces a novel multi-qubit phase-shift keying (PSK) encoding framework to enhance both fidelity and reliability in quantum image transmission. In the proposed system, source-encoded images (JPEG/HEIF) are converted into bitstreams, segmented into varying qubit sizes from 1 to 8, and mapped onto multi-qubit states using quantum PSK modulation. By exploiting multi-qubit superposition and phase modulation, the scheme improves spectral efficiency while maintaining resilience to channel noise. The encoded quantum states are transmitted through noisy channels and reconstructed via inverse quantum operations combined with classical post-processing to recover the original images. Experimental results demonstrate substantial performance improvements, evaluated using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI). Compared to superposition-only approaches, the proposed method achieves up to 3 dB SNR gain for higher qubit sizes, while single-qubit encoding remains limited due to reduced phase utilization. Moreover, relative to classical communication systems, the proposed multi-qubit PSK scheme consistently outperforms across all tested qubit sizes, highlighting its effectiveness for reliable, efficient, and high-fidelity quantum image transmission.
Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems.
Achieving reliable high-fidelity image transmission over dynamic channels remains a critical challenge in modern communication systems, particularly in vehicular environments with significant noise impairments. In response, this paper proposes a novel quantum multiple description coding (Q-MDC) framework to enhance image transmission robustness and reconstruction quality. The proposed approach partitions the source image at the pixel level into odd and even components to generate two primary descriptions, which are subsequently converted into bitstreams using various source encoding techniques, including JPEG, JPEG2000, HEIF, and uncompressed formats. These bitstreams are optionally channel encoded and encoded into quantum states in the computational basis. A third correlated description is generated using a controlled-NOT operation, embedding inter-description redundancy while maintaining equal bandwidth across all descriptions. Each description is transformed into quantum superposition states using a Hadamard-based encoder and transmitted over independent noisy channels. At the receiver, inverse Hadamard operations restore the computational basis representation. A packet-wise and qubit-wise deterministic recovery mechanism is then employed to reconstruct corrupted descriptions by exploiting inter-description dependencies. The recovered descriptions are measured, followed by channel decoding and source decoding to reconstruct the image. Simulation results demonstrate that the proposed Q-MDC framework significantly improves peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI) compared to classical MDC and channel-coded single-stream transmission schemes, achieving SNR gains of up to 4 dB and 7 dB, respectively. The results highlight the effectiveness of the proposed approach for robust multimedia transmission in error-prone wireless environments.
Reliable, high-fidelity image transmission in consumer electronics remains a challenge, particularly over noisy or bandwidth-limited channels. While quantum communication offers potential solutions, conventional single-qubit encoding schemes are limited by low state diversity and noise resilience, reducing their effectiveness for compressed image transmission. To address this, we propose Complex-Valued Orthogonal Unitary Superposition (COUS) encoding, which employs complex-valued superposition to generate highly noise-resilient quantum states within a single-qubit framework. The images are first source encoded using JPEG or HEIF and undergo classical channel coding. The resulting data are quantum encoded via COUS and transmitted over noisy channels. At the receiver, quantum states are sequentially COUS-decoded, channel-decoded, and source-decoded to reconstruct the images. System performance is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Quality Index (UQI). The results show that COUS encoding significantly improves image quality and noise resilience, achieving up to 3 dB channel SNR gain over advanced Quantum Fourier Transform (QFT)-based encodings, 4 dB gain over Hadamard encoding, and 6 dB gain relative to equivalent-bandwidth classical systems, demonstrating its potential for reliable, high-fidelity quantum image transmission regardless of image format.
Multi-qubit encoding is a quantum encoding technique that uses multiple qubits to represent information, enabling higher data throughput. This study explores its application in compressed image transmission, comparing its performance to single-qubit encoding in both uncoded and channel-coded configurations. For image compression, JPEG and HEIF formats are used, with source coding rates adjusted to ensure similar bandwidth utilization. The compressed bitstream is optionally protected by channel coding before undergoing quantum encoding using qubit sizes from 1 to 8 for quantum channel transmission. At the receiver, quantum decoding reconstructs classical bits, followed by optional channel decoding, and finally, source decoding restores the image. Image quality is assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and universal quality index (UQI). Results show that increasing the qubit encoding size improves noise resilience, with eight-qubit encoding achieving a maximum channel SNR gain of 15 dB for JPEG and 17 dB for HEIF under channel coding, demonstrating greater robustness compared to single-qubit encoding but with higher computational complexity. To address this, an adaptive multi-qubit encoding process is proposed, dynamically adjusting qubit usage by utilizing fewer qubits for lower noise levels and higher qubits for higher noise conditions while balancing noise resilience and computational complexity under the assumption of perfect channel estimation.