Q-Interference: Memory-Efficient Phase-Aware Quantum-Inspired Attention

• URL: http://arxiv.org/abs/2608.17288v1
• Date: Tue, 18 Aug 2026 02:38:17 GMT
• Title: Q-Interference: Memory-Efficient Phase-Aware Quantum-Inspired Attention
• Abstract summary: GPT attention measures token compatibility through dot-product similarity.<n>It does not explicitly model whether strong token features should reinforce or suppress one another.<n>We introduce Q-Interference, a fully classical quantum-inspired attention mechanism for autoregressive language modeling.
• Score: 10.26086309142631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: GPT attention measures token compatibility through dot-product similarity. This mechanism is simple, effective, and memory-efficient. But it does not explicitly model whether strong token features should reinforce or suppress one another. We introduce Q-Interference, a fully classical quantum-inspired attention mechanism for autoregressive language modeling that augments each query and key feature with an amplitude and a learned phase. The resulting attention score is phase-aware which aligned phases contribute constructively while conflicting phases contribute destructively. Although Q-Interference yields a richer interaction rule than similarity alone, a naive implementation of Q-Interference requires a large token-pair-feature interaction tensor, making it memory-intensive and often impractical. To address this limitation, we propose an exact trigonometric factorization that computes the same score using two standard matrix multiplications avoiding materialization of the large intermediate tensor. Q-Interference fits directly into a Transformer block in GPT and leaves the remainder of the model architecture and next-token prediction objective unchanged. Experiments on public benchmark datasets and baseline models show that the proposed reformulation trains stably in a controlled GPT-style setting and provides a consistent memory advantage over naive phase-aware interference attention. These results support the specific contribution of this work: an exact memory-efficient reformulation that makes phase-aware interference attention practical within a standard GPT pipeline.

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