Reliable high-fidelity image transmission over noisy quantum channels remains a significant challenge, especially under bandwidth constraints, where the use of quantum error correction is often impractical due to its high computational and implementation complexity. Existing encoding schemes, such as Hadamard-based encoding, often exhibit limited noise resilience and low information capacity, reducing their effectiveness for compressed image transmission in high-noise environments. To address these issues, we propose a multi-qubit quantum image transmission framework employing complex-valued orthogonal unitary superposition (COUS) encoding based on the established phase–Hadamard–phase (SHS) unitary transformation, enabling joint amplitude and phase representation for improved robust ness against noise. Input images are first compressed using JPEG or HEIF and channel-encoded using polar codes before being mapped into multi-qubit COUS states for transmission. At the receiver, quantum states are sequentially COUS decoded, channel decoded, and source decoded to reconstruct the images. The system is evaluated through simulations under noisy quantum channel conditions. System performance is analyzed using peak signal-to-noise ratio (PSNR), structural similarity index mea sure (SSIM), and universal quality index (UQI). Simulation re sults demonstrate that COUS encoding consistently outperforms Hadamard-based encoding, achieving up to 4 dB channel SNR gain even with a single-qubit configuration, while higher-qubit configurations further enhance robustness and image quality. These results highlight COUS as a scalable, low-complexity, and noise-resilient approach for high-fidelity quantum image transmission.
Reliable high-fidelity image transmission over noisy quantum channels remains a significant challenge, especially under bandwidth constraints, where the use of quantum error correction is often impractical due to its high computational and implementation complexity. Existing encoding schemes, such as Hadamard-based encoding, often exhibit limited noise resilience and low information capacity, reducing their effectiveness for compressed image transmission in high-noise environments. To address these issues, we propose a multi-qubit quantum image transmission framework employing complex-valued orthogonal unitary superposition (COUS) encoding based on the established phase–Hadamard–phase (SHS) unitary transformation, enabling joint amplitude and phase representation for improved robust ness against noise. Input images are first compressed using JPEG or HEIF and channel-encoded using polar codes before being mapped into multi-qubit COUS states for transmission. At the receiver, quantum states are sequentially COUS decoded, channel decoded, and source decoded to reconstruct the images. The system is evaluated through simulations under noisy quantum channel conditions. System performance is analyzed using peak signal-to-noise ratio (PSNR), structural similarity index mea sure (SSIM), and universal quality index (UQI). Simulation re sults demonstrate that COUS encoding consistently outperforms Hadamard-based encoding, achieving up to 4 dB channel SNR gain even with a single-qubit configuration, while higher-qubit configurations further enhance robustness and image quality. These results highlight COUS as a scalable, low-complexity, and noise-resilient approach for high-fidelity quantum image transmission.
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.
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 video transmission over noisy quantum channels remains challenging, especially due to temporal dependencies introduced by modern video compression standards. These codecs, such as versatile video coding (VVC), employ inter-frame prediction and group-of-pictures (GOP) structures, which are highly sensitive to channel noise and can lead to error propagation across frames. Conventional quantum encoding schemes, such as Hadamard-based superposition encoding, use fixed real-valued basis transformations that provide limited phase diversity and underutilize the multi-qubit state-space, reducing robustness under noisy quantum channels. To overcome these limitations, this study proposes a multi-qubit complex-valued orthogonal unitary superposition (COUS) encoding framework for quantum video transmission. In the proposed system, VVC-compressed video bitstreams are first protected using classical channel encoding, then segmented and mapped onto multi-qubit COUS quantum states, enabling joint amplitude and phase representation with improved resilience to quantum noise. At the receiver, transmitted quantum states undergo sequential COUS decoding, channel decoding, and VVC bitstream reconstruction to recover the original video frames. The simulation results show that COUS-based multi-qubit system outperforms the Hadamard encoding-based multi-qubit system, achieving peak signal-to-noise ratio (PSNR) up to 47.22 dB, structural similarity index measure (SSIM) up to 0.9905, and video multi-method assessment fusion (VMAF) up to 96.49. Even single-qubit COUS encoding achieves 3–4 dB channel SNR gain, while higher-qubit configurations further enhance robustness and reconstructed video quality. These results confirm that the proposed framework is scalable, noise-resilient, and provides high-fidelity quantum video transmission over noisy channels.
Reliable transmission of compressed images over error-prone channels remains challenging because compressed bitstreams are highly sensitive to channel impairments. Although existing quantum-inspired encoding schemes exploit superposition to improve transmission reliability, they remain vulnerable to channel noise, limiting reconstructed image quality. To address these limitations, this paper proposes a chirp transform-based multi-qubit encoding framework for robust compressed image transmission. Unlike conventional transform-based approaches, including the Hadamard transform and Quantum Fourier Transform (QFT), the proposed framework introduces nonlinear (quadratic) phase modulation into the multi-qubit representation, producing a high-dimensional superposition that enhances noise resilience through improved phase separability. This nonlinear phase variation improves error resilience through phase-induced decorrelation, reducing noise-induced distortions. The system integrates Joint Photographic Experts Group (JPEG) and High Efficiency Image File Format (HEIF) source encoding, followed by channel coding, multi-qubit encoding, and chirp transformation before transmission through a simulated quantum channel. At the receiver, inverse chirp transformation, measurement-based decoding, channel decoding, and source decoding reconstruct the transmitted 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). Experimental results demonstrate that the proposed framework achieves up to a 3 dB signal-to-noise ratio (SNR) gain over QFT-based and Hadamard-based systems in the single-qubit configuration. For the eight-qubit configuration, gains of up to 2 dB over QFT-based systems and 7 dB over Hadamard-based systems are achieved. These results demonstrate the effectiveness and scalability of the proposed framework for robust compressed image transmission over noisy quantum communication channels.
Reliable transmission of compressed images over error-prone channels remains challenging because compressed bitstreams are highly sensitive to channel impairments. Although existing quantum-inspired encoding schemes exploit superposition to improve transmission reliability, they remain vulnerable to channel noise, limiting reconstructed image quality. To address these limitations, this paper proposes a chirp transform-based multi-qubit encoding framework for robust compressed image transmission. Unlike conventional transform-based approaches, including the Hadamard transform and Quantum Fourier Transform (QFT), the proposed framework introduces nonlinear (quadratic) phase modulation into the multi-qubit representation, producing a high-dimensional superposition that enhances noise resilience through improved phase separability. This nonlinear phase variation improves error resilience through phase-induced decorrelation, reducing noise-induced distortions. The system integrates Joint Photographic Experts Group (JPEG) and High Efficiency Image File Format (HEIF) source encoding, followed by channel coding, multi-qubit encoding, and chirp transformation before transmission through a simulated quantum channel. At the receiver, inverse chirp transformation, measurement-based decoding, channel decoding, and source decoding reconstruct the transmitted 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). Experimental results demonstrate that the proposed framework achieves up to a 3 dB signal-to-noise ratio (SNR) gain over QFT-based and Hadamard-based systems in the single-qubit configuration. For the eight-qubit configuration, gains of up to 2 dB over QFT-based systems and 7 dB over Hadamard-based systems are achieved. These results demonstrate the effectiveness and scalability of the proposed framework for robust compressed image transmission over noisy quantum communication channels.
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.