Quantum communication offers a promising solution to overcome the limitations of classical communication, especially in addressing channel noise and signal fading. Inspired by classical Orthogonal Frequency Division Multiplexing (OFDM), this study introduces a novel Quantum OFDM (QOFDM) framework for image transmission over noisy quantum channels. In the proposed system, the input image bitstream is channel encoded and then quantum encoded using qubit sizes ranging from 1 to 8, optimizing the trade-off between complexity and performance. This data is processed via serial-to-parallel conversion, and the Quantum Fourier Transform (QFT) is applied to create orthogonal subcarriers. After reconversion to serial, a cyclic prefix is added to mitigate inter-symbol interference. At the receiver, the cyclic prefix is removed, the signal is converted to parallel, and Inverse QFT (IQFT) is applied to recover the quantum states. The system then performs parallel-to-serial conversion, quantum decoding, and classical reconstruction to retrieve the transmitted image. Performance evaluation using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) shows that QOFDM outperforms classical OFDM in both image quality and noise resilience. The eight-qubit configuration achieves notable channel Signal-to-Noise Ratio (SNR) gains, 22 dB in urban environments, and 16 dB in indoor settings over classical BPSK, despite higher computational demands. These results demonstrate the significant potential of QOFDM to improve multimedia communications.
Quantum communication offers a promising solution to overcome the limitations of classical communication, especially in addressing channel noise and signal fading. Inspired by classical Orthogonal Frequency Division Multiplexing (OFDM), this study introduces a novel Quantum OFDM (QOFDM) framework for image transmission over noisy quantum channels. In the proposed system, the input image bitstream is channel encoded and then quantum encoded using qubit sizes ranging from 1 to 8, optimizing the trade-off between complexity and performance. This data is processed via serial-to-parallel conversion, and the Quantum Fourier Transform (QFT) is applied to create orthogonal subcarriers. After reconversion to serial, a cyclic prefix is added to mitigate inter-symbol interference. At the receiver, the cyclic prefix is removed, the signal is converted to parallel, and Inverse QFT (IQFT) is applied to recover the quantum states. The system then performs parallel-to-serial conversion, quantum decoding, and classical reconstruction to retrieve the transmitted image. Performance evaluation using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) shows that QOFDM outperforms classical OFDM in both image quality and noise resilience. The eight-qubit configuration achieves notable channel Signal-to-Noise Ratio (SNR) gains, 22 dB in urban environments, and 16 dB in indoor settings over classical BPSK, despite higher computational demands. These results demonstrate the significant potential of QOFDM to improve multimedia communications.
Quantum communication presents new opportunities for overcoming the limitations of classical wireless systems, particularly those associated with noise, fading, and interference. Building upon the principles of classical orthogonal frequency division multi-plexing (OFDM), this work proposes a quantum OFDM architecture tailored for video transmission. In the proposed system, video sequences are first compressed using the versatile video coding (VVC) standard with different group of pictures (GOP) sizes. Each GOP size is processed through a channel encoder and mapped to multi-qubit states with various qubit configurations. The quantum-encoded data is converted from serial-to-parallel form and passed through the quantum Fourier transform (QFT) to generate mutually orthogonal quantum subcarriers. Following reserialization, a cyclic prefix is appended to mitigate inter-symbol interference within the quantum channel. At the receiver, the cyclic prefix is removed, and the signal is restored to parallel before the inverse QFT (IQFT) recovers the original quantum subcarriers. Quantum decoding, classical channel decoding, and VVC reconstruction are then employed to recover the videos. Experimental evaluations across different GOP sizes and channel conditions demonstrate that quantum OFDM provides superior resilience to channel noise and improved perceptual quality compared to classical OFDM, achieving peak signal-to-noise ratio (PSNR) up to 47.60 dB, structural similarity index measure (SSIM) up to 0.9987, and video multi-method assessment fusion (VMAF) up to 96.40. Notably, the eight-qubit encoding scheme consistently achieves the highest SNR gains across all channels, underscoring the potential of quantum OFDM as a foundation for future high-quality video transmission.
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.
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 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.
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.