Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Finite-Shot Pauli-Measurement Protocol for Calibrated Multipartite-Entanglement Diagnosis

Wenlong Sun, Yuanfeng Jin

Published Oct 21, 2026• 0 citations
Publisher

Abstract

Abstract Near-term quantum experiments often provide only finite-shot records of selected Pauli measurements, rather than tomographically complete data. We formulate multipartite-entanglement diagnosis as a calibrated decision problem from such records. The protocol combines binomial Pauli estimators, task-informed observable designs, score-based diagnostic models, class-conditional conformal prediction, and out-of-distribution abstention. It outputs a single label, a prediction set, or an abstention according to the available evidence. Synthetic benchmarks show genuine multipartite entanglement diagnosis up to 20 qubits and reveal an information hierarchy: global coherence probes support coarse diagnosis, whereas finer block-structure labels require richer local and few-body Pauli information.

DOI
10.21203/rs.3.rs-10146310/v1

Related papers

Machine Learning Methods for Detecting and Quantifying Entanglement in Multipartite Qubit Systems

TOMMASO FALASCHI

Quantum entanglement is a fundamental resource in quantum information, but its characterization becomes increasingly challenging as the size of multipartite systems grows. This thesis investigates machine learning methods to detect and quantify entanglement in pure multiqubit states using only single qubit Pauli expectation values. Two complementary tasks are considered: the distinction between fully separable and entangled states, with particular attention to weakly entangled states close to the separability boundary, and the regression of normalized multipartite classical squashed entanglement. Several machine learning models are evaluated for detection, while multilayer perceptrons are employed for quantification. The low entanglement regime is investigated through dedicated datasets and targeted data augmentation strategies. The proposed approaches are studied for systems of 5, 10, 15, and 20 qubits and are further evaluated under realistic measurement conditions, including finite shot noise, asymmetric readout errors, calibration drift, and data acquired from real quantum processors. The study assesses the scalability, robustness, and practical limitations of data-driven entanglement characterization based on a linearly scaling set of local observables.

•Sep 26, 2026•0 citations

Neural quantum states for entanglement depth certification from randomized Pauli measurements

Marcin Płodzień

Entanglement depth quantifies how many qubits share genuine multipartite entanglement, but certification typically relies on tailored witnesses or full tomography, both of which scale poorly with system size. We recast entanglement-depth and non-$k$-separability certification as likelihood-based model selection among neural quantum states whose architecture enforces a chosen entanglement constraint. A hierarchy of separable neural quantum states is trained on finite-shot local Pauli outcomes and compared against an unconstrained reference model trained on the same data. When all constrained models are statistically disfavored, the data certify entanglement beyond the imposed limit directly from measurement statistics, without reconstructing the density matrix. We validate the method on simulated six- and ten-qubit datasets targeting GHZ, Dicke, and Bell-pair states, and demonstrate robustness for mixed states under local noise. Finally, we discuss lightweight interpretability diagnostics derived from trained parameters that expose coarse entanglement patterns and qubit groupings directly from bitstring statistics.

quant-pharXiv:2512.13121•Physical Review Research•Dec 16, 2025•0 citations

Local measurement strategies for multipartite entanglement quantification

Luke Coffman, Akshay Seshadri, Graeme Smith +1 more

Despite multipartite entanglement being a global property of a quantum state, a number of recent works have made it clear that it can be quantified using only local measurements. This is appealing because local measurements are the easiest to implement on current quantum hardware. However, it remains an open question what protocol one should use in order to minimize the resources required to estimate multipartite entanglement from local measurements alone. In this work, we construct and compare several estimators of multipartite entanglement based solely on the data from local measurements. We first construct statistical estimators for a broad family of entanglement measures using local randomized measurement (LRM) data before providing a general criterion for the construction of such estimators in terms of projective 2-designs. Importantly, this allows us to derandomize the multipartite estimation protocol based on LRMs. In particular, we show how local symmetric, informationally complete positive operator-valued measures enable multipartite entanglement quantification with only a single measurement setting. For all estimators, we provide both the classical postprocessing cost and rigorous performance guarantees in the form of analytical upper bounds on the number of measurements needed to estimate the measures to any desired precision.

arXiv:2401.08065•Physical Review A•Jul 23, 2024•3 citations

Large Scale Entanglement Structure Detection in 100-Qubit Systems via Local Joint Measurements

Rui Li, Yuhang Wang, Chunxiao Du +5 more

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.

quant-pharXiv:2608.20170•Aug 21, 2026•0 citations

Measurement-Cost Triage for Non-Markovian Entanglement Diagnostics in Hyperentangled Photonic Qubits

Matthias Jakob

Abstract We formulate a layer-resolved diagnostic protocol for separating different kinds of quantum-information content in a hyperentangled four-qubit state undergoing local non-Markovian pure dephasing. The protocol uses Bell-sector coherence flow as a low-cost entrance test for candidate backflow windows, but does not identify this signal with entanglement recovery. Informative windows are then promoted to local complementarity, pair-concurrence, and simultaneous all-cut negative-partial-transpose (NPT) diagnostics. For the benchmark family studied here, the pair concurrence $C_{AB}(t)$, the local complementarity defect $D_A(t)$, and the white-noise visibility threshold $v_c(t)$ of the all-cut NPT layer have closed-form expressions. These relations show explicitly that coherence backflow, bipartite entanglement recovery, local budget redistribution, and all-cut NPT activation are distinct diagnostic statements. Robustness checks under controlled-phase, visibility, reservoir-parameter, and small state-perturbation variations support the use of the hierarchy as a quantum-information decision protocol rather than as a single state measure. The result is not a new entanglement or non-Markovianity monotone; it is an operational way to decide which information layer is certified by a given measurement record and when richer reconstructions are justified.

•Sep 8, 2026•0 citations

Task-Resolved Fisher Spectroscopy for Quantum Reservoir Computing

Yang Peng

Quantum reservoir computing (QRC) uses fixed quantum dynamics to encode a time series and trains only a classical readout, but a benchmark capacity alone does not reveal whether task information is lost in the reservoir, the measurement, feature compression, or finite sampling. We introduce \emph{task-resolved Fisher spectroscopy}, in which prediction targets define orthonormal score coordinates on the stationary distribution of input histories. Reweighting labeled histories along these scores generates an exactly affine family of reservoir states and measurement outcomes. In the same coordinates, we obtain a many-body Fisher-information hierarchy relating the state quantum Fisher information, the Fisher information of the complete measurement record, and moment matrices retained through many-body order $r$. The quadratic form of each moment matrix is exactly the stationary capacity of the optimal linear readout, while a finite-measurement extension predicts the approach from one-shot records to ideal expectation-value features. The measurement-level quantities require only stationary labeled records and measured outcome strings, not an input model or quantum-state tomography. In a five-spin open reservoir, interactions route fourth-order temporal information into higher-body correlations, so low-order compression can incur orders-of-magnitude sampling overhead even when a conventional parity target remains accessible. For correlated inputs and outputs, record-defined task scores predict held-out capacities, the required feature order, and measurement-budget dependence; optimizing the local measurement axis recovers otherwise hidden task information. The framework connects benchmark performance to experimentally actionable diagnoses of reservoir encoding, measurement choice, classical representation, and shot allocation.

quant-pharXiv:2609.29570•Aug 28, 2026•0 citations
Abstract Near-term quantum experiments often provide only finite-shot records of selected Pauli measurements, rather than tomographically complete data. We formulate multipartite-entanglement diagnosis as a calibrated decision problem from such records. The protocol combines binomial Pauli estimators, task-informed observable designs, score-based diagnostic models, class-conditional conformal prediction, and out-of-distribution abstention. It outputs a single label, a prediction set, or an abstention according to the available evidence. Synthetic benchmarks show genuine multipartite entanglement diagnosis up to 20 qubits and reveal an information hierarchy: global coherence probes support coarse diagnosis, whereas finer block-structure labels require richer local and few-body Pauli information.
Quantum entanglement is a fundamental resource in quantum information, but its characterization becomes increasingly challenging as the size of multipartite systems grows. This thesis investigates machine learning methods to detect and quantify entanglement in pure multiqubit states using only single qubit Pauli expectation values. Two complementary tasks are considered: the distinction between fully separable and entangled states, with particular attention to weakly entangled states close to the separability boundary, and the regression of normalized multipartite classical squashed entanglement. Several machine learning models are evaluated for detection, while multilayer perceptrons are employed for quantification. The low entanglement regime is investigated through dedicated datasets and targeted data augmentation strategies. The proposed approaches are studied for systems of 5, 10, 15, and 20 qubits and are further evaluated under realistic measurement conditions, including finite shot noise, asymmetric readout errors, calibration drift, and data acquired from real quantum processors. The study assesses the scalability, robustness, and practical limitations of data-driven entanglement characterization based on a linearly scaling set of local observables.
Entanglement depth quantifies how many qubits share genuine multipartite entanglement, but certification typically relies on tailored witnesses or full tomography, both of which scale poorly with system size. We recast entanglement-depth and non-kkk-separability certification as likelihood-based model selection among neural quantum states whose architecture enforces a chosen entanglement constraint. A hierarchy of separable neural quantum states is trained on finite-shot local Pauli outcomes and compared against an unconstrained reference model trained on the same data. When all constrained models are statistically disfavored, the data certify entanglement beyond the imposed limit directly from measurement statistics, without reconstructing the density matrix. We validate the method on simulated six- and ten-qubit datasets targeting GHZ, Dicke, and Bell-pair states, and demonstrate robustness for mixed states under local noise. Finally, we discuss lightweight interpretability diagnostics derived from trained parameters that expose coarse entanglement patterns and qubit groupings directly from bitstring statistics.
Despite multipartite entanglement being a global property of a quantum state, a number of recent works have made it clear that it can be quantified using only local measurements. This is appealing because local measurements are the easiest to implement on current quantum hardware. However, it remains an open question what protocol one should use in order to minimize the resources required to estimate multipartite entanglement from local measurements alone. In this work, we construct and compare several estimators of multipartite entanglement based solely on the data from local measurements. We first construct statistical estimators for a broad family of entanglement measures using local randomized measurement (LRM) data before providing a general criterion for the construction of such estimators in terms of projective 2-designs. Importantly, this allows us to derandomize the multipartite estimation protocol based on LRMs. In particular, we show how local symmetric, informationally complete positive operator-valued measures enable multipartite entanglement quantification with only a single measurement setting. For all estimators, we provide both the classical postprocessing cost and rigorous performance guarantees in the form of analytical upper bounds on the number of measurements needed to estimate the measures to any desired precision.
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 We formulate a layer-resolved diagnostic protocol for separating different kinds of quantum-information content in a hyperentangled four-qubit state undergoing local non-Markovian pure dephasing. The protocol uses Bell-sector coherence flow as a low-cost entrance test for candidate backflow windows, but does not identify this signal with entanglement recovery. Informative windows are then promoted to local complementarity, pair-concurrence, and simultaneous all-cut negative-partial-transpose (NPT) diagnostics. For the benchmark family studied here, the pair concurrence CAB(t)C_{AB}(t)CAB​(t), the local complementarity defect DA(t)D_A(t)DA​(t), and the white-noise visibility threshold vc(t)v_c(t)vc​(t) of the all-cut NPT layer have closed-form expressions. These relations show explicitly that coherence backflow, bipartite entanglement recovery, local budget redistribution, and all-cut NPT activation are distinct diagnostic statements. Robustness checks under controlled-phase, visibility, reservoir-parameter, and small state-perturbation variations support the use of the hierarchy as a quantum-information decision protocol rather than as a single state measure. The result is not a new entanglement or non-Markovianity monotone; it is an operational way to decide which information layer is certified by a given measurement record and when richer reconstructions are justified.
Quantum reservoir computing (QRC) uses fixed quantum dynamics to encode a time series and trains only a classical readout, but a benchmark capacity alone does not reveal whether task information is lost in the reservoir, the measurement, feature compression, or finite sampling. We introduce \emph{task-resolved Fisher spectroscopy}, in which prediction targets define orthonormal score coordinates on the stationary distribution of input histories. Reweighting labeled histories along these scores generates an exactly affine family of reservoir states and measurement outcomes. In the same coordinates, we obtain a many-body Fisher-information hierarchy relating the state quantum Fisher information, the Fisher information of the complete measurement record, and moment matrices retained through many-body order rrr. The quadratic form of each moment matrix is exactly the stationary capacity of the optimal linear readout, while a finite-measurement extension predicts the approach from one-shot records to ideal expectation-value features. The measurement-level quantities require only stationary labeled records and measured outcome strings, not an input model or quantum-state tomography. In a five-spin open reservoir, interactions route fourth-order temporal information into higher-body correlations, so low-order compression can incur orders-of-magnitude sampling overhead even when a conventional parity target remains accessible. For correlated inputs and outputs, record-defined task scores predict held-out capacities, the required feature order, and measurement-budget dependence; optimizing the local measurement axis recovers otherwise hidden task information. The framework connects benchmark performance to experimentally actionable diagnoses of reservoir encoding, measurement choice, classical representation, and shot allocation.