# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
###### Abstract
In response, quantum communication presents a new paradigm for data transmission, offering fundamentally different properties that can be harnessed for video delivery. Quantum superposition [6] enables qubits to encode multiple states simultaneously, thereby increasing the potential information capacity of the channel. Furthermore, quantum entanglement [7] provides correlations between qubits that classical systems cannot replicate, offering new mechanisms for coordinating and safeguarding information across a distributed system. When properly integrated, these quantum properties open up possibilities for improving throughput, enhancing noise resilience, and achieving higher reliability under adverse channel conditions. Despite the significant attention that quantum communication has received in the fields of cryptography and secure key distribution using quantum entanglement, the use of quantum superposition in media transmission, especially for dynamic video content, remains largely unexplored and untapped. In particular, the potential of quantum MIMO systems for dynamic video transmission has not been fully investigated. Although MIMO techniques, long established in classical wireless communications, improve reliability and data rates by exploiting spatial diversity through multiple transmit and receive paths, they also face several limitations, including increased hardware complexity, higher power consumption, challenges in accurate channel estimation, and reduced noise resilience. Extending MIMO principles into quantum communication could overcome these limitations and offer additional benefits by combining spatial diversity with quantum superposition. Specifically, this integration has the potential to enhance robustness against channel impairments such as fading and interference while increasing spectral efficiency. Therefore, we propose a quantum MIMO system that combines quantum superposition with spatial diversity to enable reliable video transmission over channels subject to severe fading and dynamic impairments. This work marks a pioneering effort in quantum MIMO-based video communication without prior research. To assess its effectiveness, we evaluate the system across multiple MIMO configurations, including transmit diversity (2 \(\times\) 1 MISO), receive diversity (1 \(\times\) 2 SIMO), and no diversity (1 \(\times\) 1 SISO), demonstrating the benefits of quantum-assisted spatial diversity for next-generation video transmission systems. In this proposed novel MIMO-based quantum-assisted video transmission framework, we integrate the following key components:  
* Quantum superposition-based encoding, increasing resilience to noise, and enhancing throughput. * Classical polar coding as an outer channel code, providing structured error correction for the quantum-encoded data stream. * MIMO spatial diversity techniques (including 2 \(\times\) 1 MISO, 1 \(\times\) 2 SIMO, and 1 \(\times\) 1 SISO configurations) for combating multipath fading in quantum communication channels. A detailed simulation study is presented in which the proposed framework is evaluated under Rayleigh flat-fading channel conditions across a range of signal-to-noise ratio (SNR) levels. In the proposed method, the input videos are first source-encoded to obtain compressed representations, from which the classical bitstream is extracted. This bitstream is then channel-encoded using a polar code with a rate of 1/2. The channel-encoded bitstreams are subsequently converted into quantum superposition states and transmitted through the aforementioned spatial diversity schemes over quantum channels. At the receiver, the received qubit superposition states are first quantum-decoded to recover the classical bitstreams. These are then channel-decoded and source-decoded to reconstruct the original video content. The system is evaluated using standard compressed video sequences, measuring quality through the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and video multi-method assessment fusion (VMAF).