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.
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.
ABSTRACT This work presents a quantum communication system enhanced with adaptive coding, aimed at improving compressed image transmission performance in noisy and error‐prone environments. The proposed system integrates Joint Photographic Experts Group (JPEG) and High Efficiency Image Format (HEIF) codecs with polar codes at varying code rates. To maintain consistent bandwidth usage, source coding rates are adapted according to the corresponding channel coding rates. After transmission through the quantum channel, the respective polar and source decoders reconstruct the image. 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.
As quantum communication emerges as a key technology for the future, it offers promising solutions to the limitations of classical communication systems. However, quantum communication also vulnerable to various forms of channel noise, which can introduce errors during transmission. Quantum error correction methods are critical to mitigating these errors, thereby preserving the integrity and reliability of quantum communication systems. In this study, we explore the effectiveness of the three-qubit stabilizer code for transmitting images over noisy quantum channels. We conduct a comparative analysis between the three-qubit stabilizer code and 1/3 rate polar codes, assessing their performance across uncompressed images, as well as Joint Photographic Experts Group (JPEG) and High-Efficiency Image Format (HEIF) images. Our findings indicate that the three-qubit stabilizer code offers significant advantages in quantum communication, achieving the Peak Signal-to-Noise Ratio (PSNR) up to infinity and the Structural Similarity Index Measure (SSIM) up to 1 for uncompressed images. For JPEG images, it achieves the PSNR up to 58.27 dB and the SSIM up to 0.9994, while for HEIF images, the PSNR reaches up to 64.72 dB and the SSIM up to 0.9999. These results highlight the three-qubit stabilizer code as a highly effective approach for enhancing the robustness of quantum communication 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.
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.
While quantum communication can overcome limitations of classical systems, it is limited by fragile quantum states and the complexity of underdeveloped quantum error correction (QEC) methods. This study presents an adaptive multi-qubit QEC scheme for quantum image transmission, where the code rate is dynamically adjusted based on real-time channel conditions to balance error resilience and computational complexity. For this approach, we assume that the receiver has accurate knowledge of the channel conditions to enable effective adaptation. The process begins with source encoding using JPEG and HEIF formats, followed by quantum encoding and multi-qubit QEC using three, five, seven, and nine qubits. The quantum channel encoded data are transmitted through a noisy quantum channel. At the receiver, decoding occurs in three stages: quantum channel decoding, quantum-to-classical conversion (quantum decoding), and source decoding to reconstruct the image. Experimental results show that the proposed system outperforms fixed-rate QEC methods and classical systems, achieving Peak Signal-to-Noise Ratio (PSNR) values of up to 40.51 dB, Structural Similarity Index Measure (SSIM) of up to 0.9730 and Universal Quality Index (UQI) of up to 0.9998 for JPEG images, and PSNR of up to 45.44 dB, SSIM up to 0.9889 and UQI up to 0.9999 for HEIF images.
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.