AI 中文总结
该研究提出QIG-Fusion模型，通过Transformer的量子比特互联图编码，以低计算成本预测量子电路的多体纠缠，提升量子架构搜索效率。
AI 中文摘要
多体纠缠是参数化量子电路（PQCs）的关键特性，尤其对于近期的混合量子-经典算法而言，它表征了电路生成高度纠缠态的能力。然而，测量纠缠的计算成本高昂，因为传统蒙特卡洛采样的计算量随系统规模的增长呈不利的缩放关系。为克服这一挑战，我们提出一种基于图的Transformer替代模型，可同时预测一阶Meyer-Wallach度量（$Q_1$）和二阶Scott度量（$Q_2$），解决仅用$Q_1$无法区分的纠缠结构问题。我们的核心贡献是适用于Transformer的量子比特互联图（QIG）编码，其中每个节点代表一个量子比特，带权邻接关系记录纠缠门的重数；该编码与门级有向无环图（DAG）编码器融合后得到QIG-Fusion模型。在涵盖4至8量子比特系统的50000个电路上，采用10次随机种子协议评估，QIG-Fusion的RMSE低至$Q_2$为0.037、$Q_1$为0.038，Spearman秩相关系数最高达0.95。该框架显著降低了量子架构搜索（QAS）的计算成本，为大规模参数化量子电路实现高效的纠缠估计。
英文摘要
Multipartite entanglement is a critical property of parameterized quantum circuits (PQCs), particularly for near-term hybrid quantum-classical algorithms, as it characterizes their ability to generate highly entangled states. However, measuring entanglement remains computationally expensive because conventional Monte Carlo sampling scales unfavorably with system size. To overcome this challenge, we introduce a graph-based transformer surrogate that predicts both the first-order Meyer-Wallach measure ($Q_1$) and the second-order Scott measure ($Q_2$), resolving entanglement structures indistinguishable under $Q_1$ alone. Our central contribution is the qubit-interconnected graph (QIG) encoding for transformers, where each node represents a qubit and weighted adjacencies record entangling-gate multiplicities. Fused with a gate-level DAG encoder, this yields the QIG-Fusion model. Evaluated on 50,000 circuits spanning 4- to 8-qubit systems across a ten-seed protocol, QIG-Fusion achieves an RMSE as low as 0.037 ($Q_2$) and 0.038 ($Q_1$), with a Spearman rank correlation up to 0.95. This framework significantly reduces the computational cost of Quantum Architecture Search (QAS), enabling efficient entanglement estimation for large-scale PQCs.
Comments17 pages, 12 figures
