Mechanistic interpretability aims to reverse-engineer the internal computations of Large Language Models (LLMs), yet separating sparse semantic signals from high-dimensional polysemantic noise remains a significant challenge. This paper introduces the Quantum Sieve Tracer, a hybrid quantum-classical framework designed to characterize factual recall circuits. We implement a modular pipeline that first localizes critical layers using classical causal tracing, then maps specific attention head activations into an exponentially large quantum Hilbert space. Using open-weight models (Meta Llama-3.2-1B and Alibaba Qwen2.5-1.5B-Instruct), we perform a two-stage analysis that reveals a fundamental architectural divergence. While Qwen's layer 7 circuit functions as a classic Recall Hub, we discover that Llama's layer 9 acts as an Interference Suppression circuit, where ablating the identified heads paradoxically improves factual recall. Our results demonstrate that quantum kernels can distinguish between these constructive (recall) and reductive (suppression) mechanisms, offering a high-resolution tool for analyzing the fine-grained topology of attention.

翻译：机制可解释性旨在逆向工程大语言模型的内部计算，然而从高维多义性噪声中分离稀疏语义信号仍然是一个重大挑战。本文提出了量子筛追踪器，这是一种专为刻画事实回忆电路而设计的混合量子-经典框架。我们实现了一个模块化流程：首先使用经典因果追踪定位关键层，然后将特定注意力头激活映射到指数级大的量子希尔伯特空间中。利用开源权重模型（Meta Llama-3.2-1B 与阿里巴巴 Qwen2.5-1.5B-Instruct），我们进行了两阶段分析，揭示了一个根本性的架构差异。虽然Qwen的第7层电路发挥着经典“回忆枢纽”的功能，但我们发现Llama的第9层扮演着“干扰抑制电路”的角色——切除已识别的注意力头反而能提升事实回忆性能。我们的结果表明，量子核能够区分这种建构性（回忆）与还原性（抑制）机制，为分析注意力的细粒度拓扑结构提供了一种高分辨率工具。