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Quantum image processing (QIP) is an emerging field that integrates image processing with the principles of quantum computing (QC). As quantum technologies advance, researchers face new opportunities and challenges in developing efficient QIP techniques. This paper provides an overview of quantum image representations, with a focus on two prominent encoding schemes: Novel Enhanced Quantum Representation (NEQR) and Fourier-based Quantum Image Representation (FRQI). We compare their performance in noisy quantum environments by evaluating qubit requirements, image quality, and computational efficiency. The study further analyzes the impact of quantum gate errors and qubit limitations on image reconstruction fidelity. We also compare GPU and QPU performance to highlight their strengths and weaknesses. Our findings stress the importance of error mitigation, advancements in quantum hardware, and the advancements of quantum-classical hybrid systems to drive future progress in QIP. 

Antenna designs play a crucial role in wireless communication systems where high-performance specifications are greatly required. The radiation pattern (RP) specification in both the E-plane and H-plane is important, as it connects the antenna gain along a given direction. This performance is calculated in the entire bandwidth for various frequencies and is time-consuming. To speed up these simulations, a new approach with the help of a generative adversarial network (GAN) is presented, leading to the prediction of the expected radiation pattern outcomes for the determined frequencies. This method is verified for two previously optimized antennas, one operating between 8.8-10.1 GHz and the other working in the 11.3-13.16 G Hz band. The experimental simulation results prove that the mean absolute error is less than 0.35, which yields suitable accuracy for RP predictions.