# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 5 Conclusions and Future Work  
This study introduces a quantum MIMO communication framework that aims to enhance the reliable transmission of high-quality video over wireless channels. By combining quantum superposition with classical channel coding and spatial diversity strategies, specifically SISO, MISO, and SIMO configurations within a \(2\times 2\) MIMO system, the proposed method effectively mitigates the effects of multipath fading and noise. Specifically, within this framework, compressed video sequences are encoded into quantum superposition states, enabling transmission that is more robust and resilient to fading and quantum noise. The simulation results confirm that the proposed system significantly outperforms the classical counterparts under identical channel and bandwidth conditions. Specifically, the proposed system achieves up to 39.12 dB in PSNR, 0.9471 in SSIM, and 92.47 in VMAF compared to the classical system. These improvements are consistently observed across different GOP sizes, resolutions, and frame rates, demonstrating the framework's flexibility and resilience to temporal compression variations. The architecture is designed for scalability and low complexity, making it suitable for near-future hardware implementation and practical deployment in advanced wireless communication scenarios. Furthermore, this architecture can be extended to any complex MIMO configuration based on the proven results that show promising performance.  
Future work will focus on enhancing the scalability of the proposed framework in several directions. First, the MIMO configuration can be extended to higher-order systems(e.g., 4 \(\times\) 4 and beyond), enabling greater spatial diversity and throughput. However, such expansion necessitates the development of low-complexity QEC schemes specifically tailored to quantum MIMO environments. The design of lightweight and hardware-friendly QEC algorithms will be a key research direction to preserve the reliability of the system without compromising efficiency. In addition, machine learning-based CSI prediction techniques will be explored to further optimize performance under dynamic wireless conditions. Integrating machine learning-based CSI estimation with quantum encoding can enable more adaptive and resilient transmission, particularly in highly variable or resource-constrained environments. Moreover, extending the comparison to hybrid classical-quantum frameworks, including edge-assisted video transmission and cross-layer optimization powered by AI, will provide a more comprehensive performance evaluation. These advancements aim to bridge the gap between theoretical quantum communication models and their practical realization, paving the way for efficient, scalable, and intelligent quantum-assisted wireless video transmission systems.  
Conceptualization, U.J.; methodology, U.J.; software, P.S. and T.F.; validation, U.J. and A.F.; formal analysis, A.F.; investigation, A.F.; resources, U.J.; data curation, U.J.; writing--original draft preparation, U.J.; writing--review and editing, T.F.; visualization, U.J.; supervision, A.F.; project administration, A.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original data presented in the study are openly available at [[https://www.pexels.com](https://www.pexels.com)]([https://www.pexels.com](https://www.pexels.com)) under the Creative Commons Zero (CC0) license, which allows free use, distribution, and modification without attribution.  
The authors declare no conflicts of interest.