Quantum communication leverages fundamental principles of quantum mechanics, such as superposition and measurement-induced disturbance, to enable robust information transfer under noisy conditions. In this work, a novel quantum-inspired communication framework for continuous-valued audio signal transmission is proposed and evaluated through classical simulation. The system employs Single-Qubit Probability Amplitude Modulation (SQPAM) to preserve amplitude continuity and integrates a three-qubit Quantum Inspired Error Correction scheme to mitigate channel-induced bit-flip errors, while channel impairments are modeled using a Monte Carlo–based Depolarizing noise process for controlled evaluation across varying noise levels. At the receiver, majority-voting decoding followed by inverse SQPAM mapping reconstructs the transmitted audio signal. Performance evaluation using objective reconstruction measures, perceptual quality metrics, and quantum-domain fidelity analysis demonstrates significant improvements over non-error-corrected and classical repetition-based baselines, achieving STOI values up to 0.9984 and providing an effective SNR advantage of approximately 2–3 dB, thereby highlighting the robustness and practical potential of the proposed framework for high-fidelity audio transmission in noise-limited environments.
Quantum communication leverages fundamental principles of quantum mechanics, such as superposition and measurement-induced disturbance, to enable robust information transfer under noisy conditions. In this work, a novel quantum-inspired communication framework for continuous-valued audio signal transmission is proposed and evaluated through classical simulation. The system employs Single-Qubit Probability Amplitude Modulation (SQPAM) to preserve amplitude continuity and integrates a three-qubit Quantum Inspired Error Correction scheme to mitigate channel-induced bit-flip errors, while channel impairments are modeled using a Monte Carlo–based Depolarizing noise process for controlled evaluation across varying noise levels. At the receiver, majority-voting decoding followed by inverse SQPAM mapping reconstructs the transmitted audio signal. Performance evaluation using objective reconstruction measures, perceptual quality metrics, and quantum-domain fidelity analysis demonstrates significant improvements over non-error-corrected and classical repetition-based baselines, achieving STOI values up to 0.9984 and providing an effective SNR advantage of approximately 2–3 dB, thereby highlighting the robustness and practical potential of the proposed framework for high-fidelity audio transmission in noise-limited environments.
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.
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.
Quantum communication offers transformative potential for media transmission by addressing the limitations of classical communication systems. To realize this potential, the study proposes a quantum communication framework for transmitting compressed videos over error-prone channels, leveraging quantum superposition. Two channel coding schemes are analyzed: quantum error correction (three-qubit, five-qubit, and seven-qubit codes) and classical error correction (1/3 rate polar code), all operating within the same bandwidth constraints. The proposed systems are benchmarked against a classical communication system using 1/3 rate polar codes. Results show that the three-qubit error correction-based quantum communication system, while simple and efficient, achieves significant performance gains over both classical error correction-based quantum and classical communication systems, with up to 41.42 dB in peak signal-to-noise ratio (PSNR), 0.9639 in structural similarity index measure (SSIM), and 94.4042 in video multimethod assessment fusion (VMAF). However, the five-qubit and seven-qubit systems outperform the three-qubit system, with the seven-qubit system surpassing all others in high noise environments, demonstrating its robustness across various group of pictures (GOP) formats. These findings highlight the trade-offs between simplicity and complexity, as the three-qubit system is practical and efficient, while the five-qubit and seven-qubit channel codes offer higher fidelity and resilience at the cost of increased complexity.
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.
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.