Manifestation of quantum images using unitary matrix encoding

Josh Green, Jingbo B. Wang

Udara Jayasinghe, Thanuj Fernando, Anil Fernando

Marina Lisnichenko, Stanislav Protasov

Udara Thilini Darshika Jayasinghe Mudalige, Thanuj Fernando, Anil Fernando

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.

Fei Yan, Salvador Elías Venegas-Andraca

Rafał Potempa, Michał Kordasz, Józef P. Cyran +2 more

This work presents a Python framework named after the General Equation of Quantum Image Encoding (GEQIE). The framework creates the image-encoding state using a unitary gate, which can later be transpiled to target quantum backends. The benchmarking results, simulated with different noise levels, demonstrate the correctness of the already implemented encoding methods and the usability of the framework for more sophisticated research tasks based on quantum image encodings. Additionally, we present a showcase example of Cosmic Web dark-matter density snapshot encoding and high-accuracy retrieval (PCC = 0.995) to demonstrate the extendability of the GEQIE framework to multidimensional data and its applicability to other fields of research.