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This research presents a scalable framework for detecting entanglement structures in up to 100-qubit systems using local joint measurements, effectively bypassing the exponential scaling limitations of conventional tomography. While noisy simulations achieve over 95% accuracy, experimental validation on current superconducting processors is currently limited to approximately 13 qubits due to hardware-induced noise and circuit depth constraints. These findings clearly delineate the practical boundaries for characterizing many-body quantum states on near-term devices and offer a concrete strategy for future hardware improvements.
Large Scale Entanglement Structure Detection in 100-Qubit Systems via Local Joint Measurements
arXiv:2608.20170v1 Announce Type: new Abstract: 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.
Abstract: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.
arXiv:2608.20170 [quant-ph]
(or arXiv:2608.20170v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.20170 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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