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 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.
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.
This paper proposes a quantum multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) framework for image transmission, which combines quantum multi-qubit encoding with spatial and frequency diversity to enhance noise resilience and image quality. The system utilizes joint photographic experts group (JPEG), high efficiency image file format (HEIF), and uncompressed images, which are first source-encoded (if applicable) and then processed using classical channel encoding. The channel-encoded bitstream is mapped into quantum states via multi-qubit encoding and transmitted through a 2 × 2 MIMO system with varied diversity schemes. The spatially mapped qubits undergo the quantum Fourier transform (QFT) to form quantum OFDM subcarriers, with a cyclic prefix added before transmission over fading quantum channels. At the receiver, the cyclic prefix is removed, the inverse QFT is applied, and the quantum MIMO decoder reconstructs spatially diverged quantum states. Then, quantum decoding reconstructs the bitstreams, followed by channel decoding and source decoding to recover the final image. Experimental results show that the proposed quantum MIMO-OFDM system outperforms its classical counterpart across all evaluated diversity configurations. It achieves peak signal-to-noise ratio (PSNR) values up to 58.48 dB, structural similarity index measure (SSIM) up to 0.9993, and universal quality index (UQI) up to 0.9999 for JPEG; PSNR up to 70.04 dB, SSIM up to 0.9998, and UQI up to 0.9999 for HEIF; and near-perfect reconstruction with infinite PSNR, SSIM of 1, and UQI of 1 for uncompressed images under high channel noise. These findings establish quantum MIMO-OFDM as a promising architecture for high-fidelity, bandwidth-efficient quantum multimedia communication.
Adaptive coding in quantum communication offers a promising approach to enhance the efficiency and reliability of data transmission using quantum superposition, particularly in noisy and error-prone channels. This study investigates the effectiveness of quantum communication combined with adaptive coding for compressed image transmission using Joint Photographic Experts Group (JPEG) codec and High Efficiency Image Format (HEIF) with polar codes at varying rates. To maintain a similar bandwidth, the source coding rates are adjusted according to the channel coding rates. The results show that the adaptive coding based quantum communication system significantly outperforms equivalent classical systems, especially at low signal-to-noise ratios (SNR), achieving Peak Signal-to-Noise Ratio (PSNR) improvements up to 65 dB and Structural Similarity Index Measure (SSIM) values up to 0.9999 for HEIF images and PSNR values up to 58 dB with SSIM values up to 0.9994 for JPEG images. These findings demonstrate the superior robustness and higher image quality of adaptive coding based quantum communication in varying channel noise conditions and bandwidth restricted applications.
Efficient and reliable transmission of compressed images over noisy channels remains a significant challenge due to the high sensitivity to noise. Quantum communication offers a promising solution by encoding classical information into quantum states; however, these states are still susceptible to noise and quantum decoherence. To address these limitations, we propose a complex-valued orthogonal unitary superposition (COUS) encoding integrated with a three-qubit quantum error correction (QEC) framework for robust and low-complexity quantum image transmission. The COUS encoding preserves both amplitude and phase information, enhancing reconstruction fidelity while maintaining practical scalability. In the proposed system, images are first compressed using either the joint photographic experts group (JPEG) standard or the high-efficiency image file (HEIF) standard and encoded into quantum states. Quantum channel coding is then applied to protect against quantum noise, followed by COUS encoding prior to transmission. At the receiver, the transmitted data undergoes COUS decoding, quantum error correction, quantum decoding, and source decoding to reconstruct the images. Performance improvements are observed across peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI) metrics. Simulation results demonstrate that the proposed approach outperforms conventional Hadamard encoding-based three-qubit QEC schemes, achieving maximum channel signal-to-noise ratio (SNR) gains of up to 6 dB, and surpasses bandwidth-equivalent classical communication systems employing polar codes, achieving channel SNR gains of up to 12 dB. These results highlight the potential of the proposed method as a practical solution for high-fidelity quantum image communication, overcoming the limitations of existing approaches.
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.
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.