Researchers have proposed Q-Interference, a quantum-inspired attention mechanism for language modeling that takes into account the phase relationships between token features. Unlike traditional similarity-based attention mechanisms, Q-Interference considers whether strong features should reinforce or suppress each other. However, its naive implementation requires significant memory resources. To address this issue, the authors developed an exact trigonometric factorization that computes the same score using standard matrix multiplications, reducing memory usage. Experiments on public datasets show that this reformulation provides a consistent memory advantage over traditional phase-aware interference attention. --- Why it matters: This work matters to researchers in AI because it presents a more efficient and practical way to implement phase-aware interference attention in language models, which can improve their performance and reduce computational costs. Source: https://arxiv.org/abs/2608.17288