Detecting quantum entanglement with unsupervised learning

@article{Chen2021DetectingQE,
  title={Detecting quantum entanglement with unsupervised learning},
  author={Yiwei Chen and Yu Pan and Guofeng Zhang and Shuming Cheng},
  journal={Quantum Science \& Technology},
  year={2021},
  volume={7}
}
Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in… 
View on IOP Publishing
doi.org
11 Citations
Background Citations
3

11 Citations

Entanglement detection with artificial neural networks

This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods and encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset.

Two-Qutrit entanglement: 56-years old algorithm challenges machine learning

Classifying states as entangled or separable is a highly challenging task, while it is also one of the foundations of quantum information processing theory. This task is higly nontrivial even for…

Certifying Unknown Genuine Multipartite Entanglement by Neural Networks

This work shows that neural networks can provide a nice solution to this problem, where measurement statistics data produced by measuring involved quantum states with local measurement devices serve as input features of neural networks.

Quantification of entanglement with Siamese convolutional neural networks

It is shown that enforcing entanglement-preserving symmetry operations (local operations on qubit or permutations of qubits) by using triple Siamese network, can significantly increase the model performance and ability to generalize on types of states not seen during the training stage, proving the scalability of the proposed approach.

Quantum variational learning for entanglement witnessing

This work made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness, and may pave the way to an efficient combination of QML algorithms and quantum information protocols, possibly outperforming classical approaches to analyse quantum data.

Quantum neural network autoencoder and classifier applied to an industrial case study

This work proposes a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni's Oil Treatment Plants.

New results of quasi-projective synchronization for fractional-order complex-valued neural networks with leakage and discrete delays

Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver.

The recognition of entanglement states is a notoriously difficult problem when no prior information is available. Here, we propose an efficient quantum adversarial bipartite entanglement detection…

Systematic Literature Review: Quantum Machine Learning and its applications

A review of the literature published between 2017 and 2021 to identify, analyze and classify the different types of algorithms used in quantum machine learning and their applications and shows their implementation using computational quantum circuits or ansatzs.

Quantum Verification and Estimation with Few Copies

As quantum technologies advance, the ability to generate increasingly large quantum states has experienced rapid development. In this context, the verification and estimation of large entangled…

References

SHOWING 1-10 OF 55 REFERENCES

Wasserstein Generative Adversarial Networks

This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.

Transforming Bell's Inequalities into State Classifiers with Machine Learning

It is shown that a suitably trained artificial neural network can find the optimal form of Bell’s inequality to efficiently determine if an unknown bipartite or tripartite state is entangled or separable.

Generative Adversarial Nets

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a…

Generative adversarial nets Advances in Neural Information Processing Systems pp 2672–80

  • 2014

Harnessing the Power of the Second Quantum Revolution

The second quantum revolution has been built on a foundation of fundamental research at the intersection of physics and information science, giving rise to the discipline we now call Quantum…

Siamese Neural Networks: An Overview

  • D. Chicco
  • Computer Science
    Artificial Neural Networks, 3rd Edition
  • 2021
The siamese neural network architecture is described, and its main applications in a number of computational fields since its appearance in 1994 are outlined, including the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.

Quantum anomaly detection with density estimation and multivariate Gaussian distribution

A quantum procedure for efficiently estimating the determinant of any Hermitian operators with time complexity $O(poly\log N)$ which forms an important subroutine in the authors' quantum anomaly detection with multivariate Gaussian distribution.

Receiver operating characteristics curves and related decision measures: A tutorial

Support Vector Data Description

Quantum repeaters based on entanglement purification

A scheme is presented that allows entanglement purification over arbitrary long channels and tolerates errors on the percent level and requires a polynomial overhead in time, and an overhead in local resources that grows only logarithmically with the length of the channel.
...