QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling

Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through attention mechanisms, but their quadratic complexity with respect to sequence length limits scalability to long contexts. State-space models (SSMs) provide...