Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks

• URL: http://arxiv.org/abs/2310.20570v1
• Date: Tue, 31 Oct 2023 16:00:25 GMT
• Title: Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks
• Authors: Xiaoting Gao, Mathieu Isoard, Fengxiao Sun, Carlos E. Lopetegui, Yu Xiang, Valentina Parigi, Qiongyi He, and Mattia Walschaers
• Abstract summary: Entanglement in continuous-variable non-Gaussian states provides irreplaceable advantages in many quantum information tasks. We develop a neural network that allows us to use correlation patterns to effectively detect continuous-variable entanglement.
• Score: 1.5091188291530049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Entanglement in continuous-variable non-Gaussian states provides irreplaceable advantages in many quantum information tasks. However, the sheer amount of information in such states grows exponentially and makes a full characterization impossible. Here, we develop a neural network that allows us to use correlation patterns to effectively detect continuous-variable entanglement through homodyne detection. Using a recently defined stellar hierarchy to rank the states used for training, our algorithm works not only on any kind of Gaussian state but also on a whole class of experimentally achievable non-Gaussian states, including photon-subtracted states. With the same limited amount of data, our method provides higher accuracy than usual methods to detect entanglement based on maximum-likelihood tomography. Moreover, in order to visualize the effect of the neural network, we employ a dimension reduction algorithm on the patterns. This shows that a clear boundary appears between the entangled states and others after the neural network processing. In addition, these techniques allow us to compare different entanglement witnesses and understand their working. Our findings provide a new approach for experimental detection of continuous-variable quantum correlations without resorting to a full tomography of the state and confirm the exciting potential of neural networks in quantum information processing.

Related papers

• Learning topological states from randomized measurements using variational tensor network tomography [0.4818215922729967]
  Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. We implement and study a tomographic method that combines variational optimization on tensor networks with randomized measurement techniques. We demonstrate its ability to learn the ground state of the surface code Hamiltonian as well as an experimentally realizable quantum spin liquid state.
  arXiv  Detail & Related papers  (2024-05-31T21:05:43Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• 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)
• Finding efficient observable operators in entanglement detection via convolutional neural network [2.0184593863282916]
  We devise a branching convolutional neural network which can be applied to detect entanglement in 2-qubit quantum system. Here, we detect the entanglement of Werner state, generalized Werner state and general 2-qubit states, and observable operators which are appropriate for detection can be automatically found.
  arXiv  Detail & Related papers  (2022-05-26T13:59:07Z)
• Flexible learning of quantum states with generative query neural networks [4.540894342435848]
  We show that learning across multiple quantum states can be achieved by a generative query neural network. Our network can be trained offline with classically simulated data, and later be used to characterize unknown quantum states from real experimental data.
  arXiv  Detail & Related papers  (2022-02-14T15:48:27Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Unsupervised learning of correlated quantum dynamics on disordered lattices [0.0]
  Quantum particles co-propagating on disordered lattices develop complex non-classical correlations due to an interplay between quantum statistics, inter-particle interactions, and disorder. We present a deep learning algorithm capable of learning these correlations and identifying the physical control parameters in a completely unsupervised manner.
  arXiv  Detail & Related papers  (2021-10-13T17:48:11Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Identifying nonclassicality from experimental data using artificial neural networks [52.77024349608834]
  We train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions. We show that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light.
  arXiv  Detail & Related papers  (2021-01-18T15:12:47Z)
• Classification and reconstruction of optical quantum states with deep neural networks [1.1470070927586016]
  We apply deep-neural-network-based techniques to quantum state classification and reconstruction. We demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. We present further demonstrations of our proposed [arXiv:2008.03240] QST technique with conditional generative adversarial networks (QST-CGAN)
  arXiv  Detail & Related papers  (2020-12-03T18:58:16Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)

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.