Hadamard Transform

The Hadamard Transform (HT), a fast and efficient orthogonal transform, is finding increasing application in various fields, primarily focused on reducing computational complexity while maintaining or improving performance in machine learning tasks. Current research emphasizes its use in parameter-efficient fine-tuning of large language and vision models, enhancing feature extraction for challenging datasets (e.g., dysarthric speech), and accelerating convolutional neural network operations through hybrid quantum-classical approaches. These advancements demonstrate the HT's potential to improve the efficiency and scalability of machine learning algorithms across diverse applications, from natural language processing to computer vision and signal processing.