Entanglement classification via neural network quantum states

  title={Entanglement classification via neural network quantum states},
  author={Cillian Harney and Stefano Pirandola and Alessandro Ferraro and Mauro Paternostro},
  journal={New Journal of Physics},
The task of classifying the entanglement properties of a multipartite quantum state poses a remarkable challenge due to the exponentially increasing number of ways in which quantum systems can share quantum correlations. Tackling such challenge requires a combination of sophisticated theoretical and computational techniques. In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states… 

Finding semi-optimal measurements for entanglement detection using autoencoder neural networks

This work uses autoencoder neural networks to find semi-optimal set of incomplete measurements that are most informative for the detection of entangled states and develops a neural network that can identify all two-qubits entangled states almost perfectly.

Classification and reconstruction of optical quantum states with deep neural networks

Deep-neural-network-based techniques are applied to quantum state classification and reconstruction and it is shown that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection.

Neural-network-based parameter estimation for quantum detection

This work demonstrates that adequately trained NNs enable to characterize a target with minimal knowledge of the underlying physical model in regimes where the quantum sensor presents complex responses and under a significant shot noise due to a reduced number of measurements.

Machine Learning meets Quantum Foundations: A Brief Survey

The representative works done so far at the interface of machine learning and quantum foundations are compiled to conclude the survey with potential future directions.

Learning quantum Hamiltonians from single-qubit measurements

Liangyu Che,1, 2 Chao Wei,1, 2 Yulei Huang,1, 2 Dafa Zhao,3 Shunzhong Xue,3 Xinfang Nie,1, 2 Jun Li,1, 2, ∗ Dawei Lu,1, 2, † and Tao Xin1, 2, ‡ Shenzhen Institute for Quantum Science and Engineering

Multiclass Classification of Metrologically Resourceful Tripartite Quantum States with Deep Neural Networks

The utility of artificial neural networks for classifying the entanglement of tripartite quantum states into fully separable, biseparable, and fully entangled states is explored and Bell’s inequality is employed.

Machine Learning Experimental Multipartite Entanglement Structure

With the rapid growth of controllable qubits in recent years, experimental multipartite entangled states can be created with high fidelity in various moderate‐ and large‐scale physical systems.

Deep Learning of Quantum Entanglement

This work tackles the problem of inferring the entanglement from incomplete measurements by employing deep neural networks and shows that these networks are very good at inferring quantum correlations in physical systems.

Neural-network quantum state tomography

It is confirmed that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states.

Geometric measure of entanglement from Wehrl Moments using Artificial Neural Networks

This work analyzes to what extent ANNs can provide an accurate estimate of the geometric measure of entanglement of symmetric multiqubit states on the basis of a few Wehrl moments (moments of the Husimi function of the state).



Quantum Entanglement in Neural Network States

The results uncover the unparalleled power of artificial neural networks in representing quantum many-body states, which paves a novel way to bridge computer science based machine learning techniques to outstanding quantum condensed matter physics problems.

Machine-Learning Quantum States in the NISQ Era

The theory of the restricted Boltzmann machine is discussed in detail and its practical use for state reconstruction is demonstrated, starting from a classical thermal distribution of Ising spins, then moving systematically through increasingly complex pure and mixed quantum states.

Neural-network quantum state tomography

It is demonstrated that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements, and can benefit existing and future generations of devices.

Latent Space Purification via Neural Density Operators.

This work parametrize a density matrix based on a restricted Boltzmann machine that is capable of purifying a mixed state through auxiliary degrees of freedom embedded in the latent space of its hidden units, achieving fidelities competitive with standard techniques.

Quantum Neural Network States: A Brief Review of Methods and Applications

The progress in using artificial neural networks to build quantum many‐body states is reviewed, and the Boltzmann machine representation is taken as a prototypical example to illustrate various aspects of the neural network states.

Learnability scaling of quantum states: Restricted Boltzmann machines

This work empirically study the scaling of restricted Boltzmann machines (RBMs) applied to reconstruct ground-state wavefunctions of the one-dimensional transverse-field Ising model from projective measurement data and finds that the number of weights can be significantly reduced while still retaining an accurate reconstruction.

Solving the quantum many-body problem with artificial neural networks

A variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons and a reinforcement-learning scheme that is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems.

Geometric measure of entanglement and applications to bipartite and multipartite quantum states

The degree to which a pure quantum state is entangled can be characterized by the distance or angle to the nearest unentangled state. This geometric measure of entanglement, already present in a

An introduction to quantum machine learning

This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Measurement-based quantum computation

Quantum computation offers a promising new kind of information processing, where the non-classical features of quantum mechanics are harnessed and exploited. A number of models of quantum computation