AI 中文总结
本研究提出可扩展框架，通过满足边界匹配条件的局域泡利基测量，以与系统尺寸无关的工作量分类多量子比特纠缠结构，在模拟与超导处理器上验证了其性能并划定硬件可扩展性边界。
AI 中文摘要
识别多体量子态的纠缠结构，即其组分如何划分为无纠缠块，是量子信息科学的核心任务，然而常规层析成像随系统尺寸呈指数级缩放。本文提出一种可扩展框架，直接从局域关联指纹识别大规模纠缠结构。通过选择满足边界匹配条件p₁=p_R的代表性局域泡利基，整个链可在单一测量构型中读出，使测量工作量与系统尺寸无关。在噪声模拟中，该单基协议对30个候选划分中的GHZ型、W型及团簇型结构进行分类，在最多100量子比特的系统中平均准确率超95%。我们进一步在超导量子处理器上验证该协议，在更大尺寸的噪声及深度诱导退化出现前，其可可靠分类最多13量子比特系统的块结构。通过明确映射这些失效模式，本研究结果划定了硬件级可扩展性的边界，并为在近期量子设备上表征纠缠结构指明了具体策略。
英文摘要
Identifying the entanglement structure of a many-body quantum state, namely how its constituents partition into unentangled blocks, is a central task in quantum information science, yet conventional tomography scales exponentially with system size. Here we introduce a scalable framework that recognizes large-scale entanglement structures directly from local correlation fingerprints. By choosing a representative local Pauli basis that satisfies a boundary-matching condition p_1 = p_R, the entire chain is read out in a single measurement configuration, keeping the measurement effort independent of system size. In noisy simulations, this single-basis protocol classifies GHZ-, W-, and cluster-type structures among 30 candidate partitions with a mean accuracy exceeding 95% for systems of up to 100 qubits. We further validate the protocol on a superconducting quantum processor, where it reliably classifies block structures for systems of up to 13 qubits before noise- and depth-induced degradation sets in at larger sizes. By mapping these failure modes explicitly, our results delineate the boundary of hardware-level scalability and point to a concrete strategy for characterizing entanglement structure on near-term quantum devices.
