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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. 

Inspired by the human brain, Hyperdimensional Computing (HDC) processes information efficiently by operating in high-dimensional space using hypervectors. While previous works focus on optimizing pre-generated hypervectors in software, this study introduces a novel on-the-fly vector generation method in hardware with O(1) complexity, compared to the O(N) iterative search used in conventional approaches to find the best orthogonal hypervectors. Our approach leverages Hadamard binary coefficients and unary computing to simplify encoding into addition-only operations after the generation stage in ASIC, implemented using in-memory computing. The proposed design significantly improves accuracy and computational efficiency across multiple benchmark datasets.