A new classical Transformer architecture, QiT, is proposed to mimic the structure of quantum neural networks for visual recognition tasks. QiT uses trigonometric Hilbert-space features and cosine kernel approximation to achieve competitive performance without relying on quantum computation or speedup. This development may impact the design of classical AI models and their performance in computer vision tasks.