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arXiv:1810.09331 · Scientific Reports

Optimal estimation of entanglement and discord in two-qubit states

Salvatore Virzì, Enrico Rebufello, Alessio Avella, Fabrizio Piacentini, Marco Gramegna, Ivano Ruo Berchera, Ivo Pietro Degiovanni, Marco Genovese

Published Feb 28, 2019• 20 citations
arXivHTMLPDF

Abstract

Recently, the fast development of quantum technologies led to the need for tools allowing the characterization of quantum resources. In particular, the ability to estimate non-classical aspects, e.g. entanglement and quantum discord, in two-qubit systems, is relevant to optimise the performance of quantum information processes. Here we present an experiment in which the amount of entanglement and discord are measured exploiting different estimators. Among them, some will prove to be optimal, i.e., able to reach the ultimate precision bound allowed by quantum mechanics. These estimation techniques have been tested with a specific family of states ranging from nearly pure Bell states to completely mixed states. This work represents a significant step towards the development of reliable metrological tools for quantum technologies.

DOI
10.1038/s41598-019-39334-8

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Abstract Quantum discord characterizes non-classical correlations between two or more subsystems that persist even in the absence of entanglement. Unlike quantum entanglement, this measure captures more general quantum correlations, yet its estimation remains challenging due to the inherent difficulty in determining the optimal measurement orientations. In this work, we present a machine learning approach to estimate quantum discord for two-qubit systems with the help of artificial neural networks. Two distinct input strategies are investigated: one involving complete quantum state information derived from the density matrix elements and the other involving partial information derived from the expected values of measurements. Numerical simulations demonstrate that both artificial neural network architectures can predict the quantum discord of new quantum states with high precision. Our findings provide a new way to estimate quantum correlations, demonstrating the effectiveness of machine learning in quantum information tasks.
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