# Entanglement classification via neural network quantum states

@article{Harney2019EntanglementCV, 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}, year={2019}, volume={22} }

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…

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## References

SHOWING 1-10 OF 35 REFERENCES

### Quantum Entanglement in Neural Network States

- Physics, Computer Science
- 2017

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

- PhysicsAnnual Review of Condensed Matter Physics
- 2020

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

- Physics, Computer Science
- 2018

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.

- Computer SciencePhysical review letters
- 2018

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

- Physics, Computer ScienceAdvanced Quantum Technologies
- 2019

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

- Physics, Computer SciencePhysical Review B
- 2019

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

- Computer Science, PhysicsScience
- 2017

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

- Physics
- 2003

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

- Computer ScienceContemporary Physics
- 2014

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

- Physics
- 2009

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…