GHZ-W Genuinely Entangled Subspace Verification with Adaptive Local Measurements

• URL: http://arxiv.org/abs/2412.19540v1
• Date: Fri, 27 Dec 2024 09:07:44 GMT
• Title: GHZ-W Genuinely Entangled Subspace Verification with Adaptive Local Measurements
• Abstract summary: We develop two adaptive verification strategies, the XZ strategy and the rotation strategy, for the three-qubit GHZ-W GES.<n>We classify the two-dimensional two-qubit subspaces into three distinct types, including unverifiable entangled subspaces.
• Score: 15.550719829709962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genuinely entangled subspaces (GESs) are valuable resources in quantum information science. Among these, the three-qubit GHZ-W GES, spanned by the three-qubit Greenberger-Horne-Zeilinger (GHZ) and W states, is a universal and crucial entangled subspace resource for three-qubit systems. In this work, we develop two adaptive verification strategies, the XZ strategy and the rotation strategy, for the three-qubit GHZ-W GES using local measurements and one-way classical communication. These strategies are experimentally feasible, efficient and possess a concise analytical expression for the sample complexity of the rotation strategy, which scales approximately as $2.248/\epsilon\ln(1/\delta)$, where $\epsilon$ is the infidelity and $1-\delta$ is the confidence level. Furthermore, we comprehensively analyze the two-dimensional two-qubit subspaces and classify them into three distinct types, including unverifiable entangled subspaces, revealing intrinsic limitations in local verification of entangled subspaces.

Related papers

• Analytic Qubit Separation between POVMs and Projective Measurements [11.317423514829237]
  We construct a Bell functional with rational coefficients that separates the two measurement models at local dimension two.<n>An explicit three-outcome qubit positive-operator-valued measure with rational matrix entries attains $2sqrt2+1/100$.<n>This is the first fully analytic Bell-functional separation between qubit POVMs and qubit projective measurements over arbitrary shared two-qubit states.
  arXiv  Detail & Related papers  (2026-08-02T15:37:41Z)
• Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention [7.887469434417159]
  We introduce a controlled subspace intervention framework to investigate how self-supervised Vision Transformers (ViTs) encode dense geometric information.<n>By connecting internal encoding mechanics with downstream performance, these findings provide a basis for effective feature selection and lightweight decoder design.
  arXiv  Detail & Related papers  (2026-07-02T10:18:02Z)
• Faster 3D Gaussian Splatting Convergence via Structure-Aware Densification [52.517763621139956]
  3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis.<n>Standard adaptive density control relies on screen-space positional gradients.<n>We present a structure-aware densification framework for 3D Gaussian Splatting.
  arXiv  Detail & Related papers  (2026-04-30T15:37:20Z)
• NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation [65.34304674634713]
  We introduce NG-GS, a framework for high-quality object segmentation in 3DGS that explicitly addresses boundary discretization.<n>We show that our method achieves photorealistic state-of-the-art performance, with significant gains in boundary mIoU.
  arXiv  Detail & Related papers  (2026-04-16T07:14:07Z)
• Deterministic Generation of Arbitrary Fock States via Resonant Subspace Engineering [9.529916381983394]
  We introduce resonant subspace engineering (RSE), a protocol that analytically confines the infinite-dimensional bosonic dynamics to a two-dimensional invariant subspace.<n>RSE achieves $O(n1/4)$ scaling in both evolution time and gate depth, showing a fundamental improvement over existing deterministic schemes.<n>RSE provides a scalable and analytically transparent framework for large-scale bosonic state engineering and gate synthesis.
  arXiv  Detail & Related papers  (2026-02-12T16:36:58Z)
• D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction [73.61056394880733]
  3D Gaussian Splatting (3DGS) enables real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations.<n>We identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage.<n>We propose a unified framework D$2$GS, comprising two key components: a Depth-and-Density Guided Dropout strategy, and a Distance-Aware Fidelity Enhancement module.
  arXiv  Detail & Related papers  (2025-10-09T17:59:49Z)
• Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing [55.291219073365546]
  Open-Vocabulary Remote Sensing Image (OVRSIS) is an emerging task that adapts Open-Vocabulary (OVS) to the remote sensing (RS) domain.<n>textbfRSKT-Seg is a novel open-vocabulary segmentation framework tailored for remote sensing.<n> RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2x faster inference through efficient aggregation.
  arXiv  Detail & Related papers  (2025-09-15T15:24:49Z)
• Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution [68.54692184478462]
  Mamba-based methods have shown great potential in optimizing both computational cost and performance of light field image super-resolution.<n>We propose a Subspace Simple Scanning (Sub-SS) strategy, based on which we design the Subspace Simple Mamba Block (SSMB) to achieve more efficient and precise feature extraction.<n>We also propose a dual-stage modeling strategy to address the limitation of state space in preserving spatial-angular and disparity information.
  arXiv  Detail & Related papers  (2025-09-05T05:50:38Z)
• Comparing GHZ-Based Strategies for Multipartite Entanglement Distribution in 2D Repeater Networks [0.17811840395202347]
  We conduct a comparative study to determine the initial quality necessary to extend the distance range of an $N$-qubit GHZ state.<n>We analyze two strategies for distributing initial GHZ states using a centralized quantum switch.
  arXiv  Detail & Related papers  (2025-05-16T18:50:50Z)
• Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity [3.792470553976718]
  This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS)<n>Topology-GS addresses compromised pixel-level structural integrity due to incomplete initial geometric coverage.<n>Experiments on three novel-view benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics.
  arXiv  Detail & Related papers  (2024-12-21T13:25:03Z)
• TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views [18.050257821756148]
  TSGaussian is a novel framework that combines semantic constraints with depth priors to avoid geometry degradation in novel view synthesis tasks. Our approach prioritizes computational resources on designated targets while minimizing background allocation. Extensive experiments demonstrate that TSGaussian outperforms state-of-the-art methods on three standard datasets.
  arXiv  Detail & Related papers  (2024-12-13T11:26:38Z)
• G2SDF: Surface Reconstruction from Explicit Gaussians with Implicit SDFs [84.07233691641193]
  We introduce G2SDF, a novel approach that integrates a neural implicit Signed Distance Field into the Gaussian Splatting framework. G2SDF achieves superior quality than prior works while maintaining the efficiency of 3DGS.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• Efficient Verification of Stabilizer Code Subspaces with Local Measurements [14.494830101269677]
  We present two efficient verification strategies for general stabilizer code subspaces.<n>New strategies require significantly fewer measurement settings and consume fewer state copies.<n>This work contributes to the first systematic study of efficient verification of stabilizer code subspaces with local measurements.
  arXiv  Detail & Related papers  (2024-09-29T13:16:12Z)
• Implicit Gaussian Splatting with Efficient Multi-Level Tri-Plane Representation [45.582869951581785]
  Implicit Gaussian Splatting (IGS) is an innovative hybrid model that integrates explicit point clouds with implicit feature embeddings. We introduce a level-based progressive training scheme, which incorporates explicit spatial regularization. Our algorithm can deliver high-quality rendering using only a few MBs, effectively balancing storage efficiency and rendering fidelity.
  arXiv  Detail & Related papers  (2024-08-19T14:34:17Z)
• Gaussian Splatting with Localized Points Management [52.009874685460694]
  Localized Point Management (LPM) is capable of identifying those error-contributing zones in the highest demand for both point addition and geometry calibration. LPM applies point densification in the identified zone, whilst resetting the opacity of those points residing in front of these regions so that a new opportunity is created to correct ill-conditioned points. Notably, LPM improves both vanilla 3DGS and SpaceTimeGS to achieve state-of-the-art rendering quality while retaining real-time speeds.
  arXiv  Detail & Related papers  (2024-06-06T16:55:07Z)
• KPZ scaling from the Krylov space [83.88591755871734]
  Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang scaling in late-time correlators and autocorrelators has been reported. Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis.
  arXiv  Detail & Related papers  (2024-06-04T20:57:59Z)
• GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane [53.388937705785025]
  3D open-vocabulary scene understanding is crucial for advancing augmented reality and robotic applications. We introduce GOI, a framework that integrates semantic features from 2D vision-language foundation models into 3D Gaussian Splatting (3DGS) Our method treats the feature selection process as a hyperplane division within the feature space, retaining only features that are highly relevant to the query.
  arXiv  Detail & Related papers  (2024-05-27T18:57:18Z)
• Improving Transmon Qudit Measurement on IBM Quantum Hardware [0.0]
  We propose and analyze two measurement strategies that improve the distinguishability of transmon qudit states. We discuss these strategies in the context of a practical implementation of the default measurement of a ququart on IBM Quantum hardware.
  arXiv  Detail & Related papers  (2023-07-25T13:58:11Z)
• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [64.11157141177208]
  We propose a spectral enhanced rectangle Transformer to model the spatial and spectral correlation in hyperspectral images. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise.
  arXiv  Detail & Related papers  (2023-04-03T09:42:13Z)
• Complete unitary qutrit control in ultracold atoms [0.0]
  We encode neutral-atom qutrits in an ensemble of ultracold $87$Rb and demonstrate arbitrary single-qutrit SU(3) gates. This work establishes the ultracold neutral-atom qutrit as a promising platform for qutrit-based quantum information processing.
  arXiv  Detail & Related papers  (2022-07-29T19:23:16Z)
• GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning [55.79997930181418]
  Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
  arXiv  Detail & Related papers  (2022-07-05T04:04:37Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Low-Rank Subspaces in GANs [101.48350547067628]
  This work introduces low-rank subspaces that enable more precise control of GAN generation. LowRankGAN is able to find the low-dimensional representation of attribute manifold. Experiments on state-of-the-art GAN models (including StyleGAN2 and BigGAN) trained on various datasets demonstrate the effectiveness of our LowRankGAN.
  arXiv  Detail & Related papers  (2021-06-08T16:16:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.