Entanglement Structure Detection via Machine Learning

• URL: http://arxiv.org/abs/2012.00526v2
• Date: Sat, 12 Dec 2020 12:58:50 GMT
• Title: Entanglement Structure Detection via Machine Learning
• Abstract summary: We propose an efficient machine learning based approach for predicting the entanglement intactness and depth simultaneously. In particular, the learned classifier can discover the entanglement intactness and depth bounds for the noised GHZ state.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting the entanglement structure, such as intactness and depth, of an n-qubit state is important for understanding the imperfectness of the state preparation in experiments. However, identifying such structure usually requires an exponential number of local measurements. In this letter, we propose an efficient machine learning based approach for predicting the entanglement intactness and depth simultaneously. The generalization ability of this classifier has been convincingly proved, as it can precisely distinguish the whole range of pure generalized GHZ states which never exist in the training process. In particular, the learned classifier can discover the entanglement intactness and depth bounds for the noised GHZ state, for which the exact bounds are only partially known.

Related papers

• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection [50.343419243749054]
  Anomaly detection is critical in fields such as medical diagnostics and industrial defect detection.<n> CLIP's coarse-grained image-text alignment limits localization and detection performance for fine-grained anomalies.<n>Crane improves the state-of-the-art ZSAD from 2% to 28%, at both image and pixel levels, while remaining competitive in inference speed.
  arXiv  Detail & Related papers  (2025-04-15T10:42:25Z)
• Bounding the Sample Fluctuation for Pure States Certification with Local Random Measurement [4.923287660970805]
  Recent advancements in randomized measurement techniques have provided fresh insights in this area. We investigate the fundamental properties of schemes that certify pure quantum states through random local Haar measurements. Our results unveil the intrinsic interplay between operator complexity and the efficiency of quantum algorithms, serving as an obstacle to local certification of pure states with long-range entanglement.
  arXiv  Detail & Related papers  (2024-10-22T02:26:44Z)
• Towards Demystifying the Generalization Behaviors When Neural Collapse Emerges [132.62934175555145]
  Neural Collapse (NC) is a well-known phenomenon of deep neural networks in the terminal phase of training (TPT) We propose a theoretical explanation for why continuing training can still lead to accuracy improvement on test set, even after the train accuracy has reached 100%. We refer to this newly discovered property as "non-conservative generalization"
  arXiv  Detail & Related papers  (2023-10-12T14:29:02Z)
• Data-driven criteria for quantum correlations [0.0]
  We build a machine learning model to detect correlations in a three-qubit system. We find that the proposed detector performs much better at distinguishing a weaker form of quantum correlations, namely, the quantum discord, than entanglement.
  arXiv  Detail & Related papers  (2023-07-20T17:59:59Z)
• Quantum state tomography with tensor train cross approximation [84.59270977313619]
  We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings. Our method requires exponentially fewer state copies than the best known tomography method for unstructured states and local measurements.
  arXiv  Detail & Related papers  (2022-07-13T17:56:28Z)
• On Leave-One-Out Conditional Mutual Information For Generalization [122.2734338600665]
  We derive information theoretic generalization bounds for supervised learning algorithms based on a new measure of leave-one-out conditional mutual information (loo-CMI) Contrary to other CMI bounds, our loo-CMI bounds can be computed easily and can be interpreted in connection to other notions such as classical leave-one-out cross-validation. We empirically validate the quality of the bound by evaluating its predicted generalization gap in scenarios for deep learning.
  arXiv  Detail & Related papers  (2022-07-01T17:58:29Z)
• Constraining Gaussian processes for physics-informed acoustic emission mapping [0.0]
  In this paper, we consider the use of physics-informed Gaussian processes for learning acoustic emission maps. We show that our approach significantly reduces the burden of data collection, where it is seen that incorporation of boundary condition knowledge significantly improves predictive accuracy.
  arXiv  Detail & Related papers  (2022-06-03T10:42:57Z)
• Learning Hidden Markov Models When the Locations of Missing Observations are Unknown [54.40592050737724]
  We consider the general problem of learning an HMM from data with unknown missing observation locations. We provide reconstruction algorithms that do not require any assumptions about the structure of the underlying chain. We show that under proper specifications one can reconstruct the process dynamics as well as if the missing observations positions were known.
  arXiv  Detail & Related papers  (2022-03-12T22:40:43Z)
• Learning entanglement breakdown as a phase transition by confusion [0.0]
  We develop an approach for revealing entanglement breakdown using a machine learning technique, which is known as 'learning by confusion' We show that the developed method provides correct answers for a variety of states, including entangled states with positive partial transpose (PPT) We also present a more practical version of the method, which is suitable for studying entanglement breakdown in noisy intermediate-scale quantum (NISQ) devices.
  arXiv  Detail & Related papers  (2022-02-01T11:41:18Z)
• Non-standard entanglement structure of local unitary self-dual models as a saturated situation of repeatability in general probabilistic theories [61.12008553173672]
  We show the existence of infinite structures of quantum composite system such that it is self-dual with local unitary symmetry. We also show the existence of a structure of quantum composite system such that non-orthogonal states in the structure are perfectly distinguishable.
  arXiv  Detail & Related papers  (2021-11-29T23:37:58Z)
• From observations to complexity of quantum states via unsupervised learning [0.0]
  We use unsupervised learning with autoencoder neural networks to detect the local complexity of time-evolved states. Our approach is an ideal diagnostics tool for data obtained from (noisy) quantum simulators because it requires only practically accessible local observations.
  arXiv  Detail & Related papers  (2021-02-22T19:44:55Z)
• Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning [93.18238573921629]
  We study how Ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model. We show that ensemble/knowledge distillation in deep learning works very differently from traditional learning theory. We prove that self-distillation can also be viewed as implicitly combining ensemble and knowledge distillation to improve test accuracy.
  arXiv  Detail & Related papers  (2020-12-17T18:34:45Z)

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.