# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
###### Abstract  
Reliable transmission of high-quality video over wireless channels is challenged by fading and noise, which degrade visual quality and disrupt temporal continuity. To address these issues, this paper proposes a quantum communication framework that integrates quantum superposition with multi-input multi-output (MIMO) spatial diversity techniques to enhance robustness and efficiency in dynamic video transmission. The proposed method converts compressed videos into classical bitstreams, which are then channel-encoded and quantum-encoded into qubit superposition states. These states are transmitted over a \(2\times 2\) MIMO system employing varied diversity schemes to mitigate the effects of multipath fading and noise. At the receiver, a quantum decoder reconstructs the classical information, followed by channel decoding to retrieve the video data, and the source decoder reconstructs the final video. Simulation results demonstrate that the quantum MIMO system significantly outperforms equivalent-bandwidth classical MIMO frameworks across diverse signal-to-noise ratio (SNR) conditions, achieving a peak signal-to-noise ratio (PSNR) up to 39.12 dB, structural similarity index (SSIM) up to 0.9471, and video multi-method assessment fusion (VMAF) up to 92.47, with improved error resilience across various group of picture (GOP) formats, highlighting the potential of quantum MIMO communication for enhancing the reliability and quality of video delivery in next-generation wireless networks. MIMO; quantum communication; quantum superposition; receive diversity; transmit diversity +
Footnote †: journal: _arXiv preprint arXiv:1907.03815_  
0000-0001-6000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-0000-00000-000  
To cope with the high bandwidth requirements of video, traditional transmission pipelines rely on source coding with advanced video compression standards such as H.264/AVC, H.265/HEVC, VP9, or AV1. These codecs achieve impressive compression efficiency by exploiting temporal redundancies across frames (through motion estimation and compensation) and spatial redundancies within frames. However, this compression strategy introduces significant vulnerability to transmission errors. A single corrupted bit, particularly in compressed bitstreams with inter-frame dependencies, can cause artifacts that propagate across multiple frames and degrade the perceived quality of the video. This problem becomes especially pronounced in time-varying and hostile channel conditions, where the combined effects of fading, interference, and noise make it challenging to maintain acceptable video quality and a smooth viewing experience. To mitigate these effects, classical communication systems employ a variety of signal processing and coding strategies. Techniques such as adaptive modulation, forward error correction (FEC), automatic repeat request (ARQ), and multi-input multi-output (MIMO) spatial diversity have demonstrated success in improving the reliability and efficiency of video transmission in wireless environments. In particular, MIMO techniques [4; 5] enhance robustness by exploiting multiple antennas at the transmitter and receiver to combat multipath fading and improve spectral efficiency. This MIMO transmission can be broadly categorized into four types: single-input single-output (SISO), which lacks spatial diversity; multi-input single-output (MISO), which employs transmit diversity; single-input multi-output (SIMO), which utilizes receive diversity; and multi-input multi-output (MIMO), which combines both transmit and receive diversity for enhanced performance. Although these diversity techniques have proven effective in enhancing data throughput and improving reliability, these classical methods encounter limitations in highly dynamic or bandwidth-constrained environments, where trade-offs between latency, complexity, and error resilience become more pronounced.