Abstract
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. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine architecture, known as Neural Network Quantum States, whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States can be used to build entanglement witnesses for any target state.
Highlights
April 2020Cillian Harney1,2 , Stefano Pirandola , Alessandro Ferraro and Mauro Paternostro
As the size of a quantum system grows, the number of accessible states, and the Hilbert space dimension, scales exponentially
We employ the recently introduced neural network quantum states (NNSs) [5], which are Artificial Neural Network (ANN) architectures of the restricted Boltzmann Machine (RBM) form, to build an accurate entanglement-separability classifier that we show to be effective in both witnessing multipartite entangled states and identify the kinseparability class of generic multipartite quantum states
Summary
Cillian Harney1,2 , Stefano Pirandola , Alessandro Ferraro and Mauro Paternostro. Any further distribution of systems can share quantum correlations. Tackling such challenge requires a combination of this work must maintain attribution to the sophisticated theoretical and computational techniques. In this paper we combine machineauthor(s) and the title of the work, journal citation learning tools and the theory of quantum entanglement to perform entanglement classification for and DOI. Network Quantum States, whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States can be used to build entanglement witnesses for any target state
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