A channel-adaptive quantum transform-domain variable-qubit encoding for quantum image transmission

Reliable image transmission over noisy quantum channels is challenging due to qubit decoherence, channel fluctuations, and resource constraints. Quantum communication in the time domain often fail to maintain high image fidelity under varying channel conditions. To address this, we propose an adaptive quantum image transmission framework using variable-qubit transform-based encoding with the Quantum Fourier Transform (QFT) and perfect channel state information. The system dynamically selects the optimal qubit size based on real-time channel conditions, balancing resource usage and reconstruction quality. Images are source-encoded (JPEG or HEIF), converted to bitstreams, optionally channel-encoded, segmented by qubit size, quantum-encoded into multi-qubit states, and transformed to the frequency domain via QFT for transmission. At the receiver, inverse QFT, measurement, and decoding recover the bitstreams, followed by source decoding to reconstruct the images. Experiments show the adaptive frequency-domain variable-qubit system outperforms fixed-qubit and adaptive time-domain schemes, achieving Peak Signal-to-Noise Ratio (PSNR) up to 58.42 dB, Structural Similarity Index Measure (SSIM) up to 0.9992, and Universal Quality Index (UQI) up to 0.9999 for JPEG, and PSNR up to 70.05 dB, SSIM up to 0.9999, and UQI up to 0.9999 for HEIF. These results demonstrate superior fidelity and noise resilience, highlighting the promise of adaptive frequency-domain quantum encoding for robust image transmission.