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.
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.
Quantum communication enables high-fidelity image transmission but is vulnerable to channel noise, and while advanced quantum error correction (QEC) can reduce such effects, its complexity and time-domain dependence limit practical efficiency. This paper presents a novel, low-complexity, and noise-resilient quantum image transmission framework that operates in the frequency domain using the quantum Fourier transform (QFT) combined with the three-qubit QEC code. In the proposed system, input images are first source-encoded (JPEG/HEIF) and mapped to quantum states using single-qubit superposition encoding. Three-qubit QEC is then applied for channel protection, effectively safeguarding the encoded data against quantum errors. The channel-encoded quantum data are subsequently transformed via QFT for transmission over noisy quantum channels. At the receiver, the inverse QFT recovers the frequency-domain representation, after which three-qubit error correction, quantum decoding, and corresponding source decoding are performed to reconstruct the image. Results are analyzed using bit error rate (BER), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Experimental results show that the proposed quantum frequency-domain approach achieves up to 4 dB channel SNR gain over equivalent quantum time-domain methods and up to 10 dB over an equivalent-bandwidth classical communication system, regardless of the image format. These findings highlight the practical advantages of integrating QFT-based transmission with lightweight QEC, offering an efficient, scalable, and noise-tolerant solution for future quantum communication networks.
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.
This paper proposes a novel quantum image transmission framework to address the limitations of existing single-qubit time domain systems, which struggle with noise resilience and scalability. The framework integrates frequency domain processing with multi-qubit (1 to 8 qubits) encoding to enhance robustness against quantum noise. Initially, images are source-coded using JPEG and HEIF formats with rate adjustment to ensure consistent bandwidth usage. The resulting bitstreams are channel-encoded and mapped to multi-qubit quantum states. These states are transformed into the frequency domain via the quantum Fourier transform (QFT) for transmission. At the receiver, the inverse QFT recovers the time domain states, followed by multi-qubit decoding, channel decoding, and source decoding to reconstruct the image. Performance is evaluated using bit error rate (BER), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Results show that increasing the number of qubits enhances image quality and noise robustness, albeit at the cost of increased system complexity. Compared to time domain processing, the frequency domain approach achieves superior performance across all qubit configurations, with the eight-qubit system delivering up to a 4 dB maximum channel SNR gain for both JPEG and HEIF images. Although single-qubit systems benefit less from frequency domain encoding due to limited representational capacity, the overall framework demonstrates strong potential for scalable and noise-robust quantum image transmission in future quantum communication networks.
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.