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International Conference on Communications

A Channel-Adaptive Quantum Transform-Domain Variable-Qubit Encoding for Quantum Image Transmission

Udara Jayasinghe, Thanuj Fernando, Anil Fernando

Published May 24, 2026• 0 citations
Publisher
DOI
10.1109/icc59461.2026.11587717

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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.
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.