Exploiting Quantum Entanglement for Sub-Quadratic Attention in Long Legal Documents

We propose QEAM, a hybrid quantum-classical attention architecture that breaks the well-known quadratic barrier in self-attention by routing information through structured entanglement circuits. Unlike prior quantum NLP work that largely reimplements classical dot-product attention on quantum hardware, our approach treats entanglement itself as the primary computational resource. The central idea is a four-level entanglement topology—spanning tokens, sentences, sections, and cross-document citations—whose wiring follows the actual rhetorical structure of legal texts. On the Indian Legal Documents Corpus (ILDC), we observe 88.7% judgment prediction accuracy, which is roughly 5 percentage points above the strongest long-context baseline we tested. Perhaps more importantly for practical deployment, the gate count scales linearly with document length, and the resulting attention maps remain fully traceable—a property that matters in jurisdictions where courts are constitutionally required to give reasons for their decisions. We discuss NISQ-era feasibility, noise budgets, and the limitations of our current simulation-based evaluation.