Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1905.10782 · Quantum Information Processing

Study on estimating quantum discord by neural network with prior knowledge

Yong-Lei Liu, An Min Wang, Yi Sun, Pengfei Zhang, Guodong Wang

Published Oct 10, 2019• 4 citations
arXivHTMLPDF
DOI
10.1007/s11128-019-2468-2

Related papers

Effective detection of quantum discord by using convolutional neural networks

Narjes Taghadomi, A. Mani, Ali Fahim +1 more

arXiv:2401.07405•Quantum Machine Intelligence•Mar 12, 2025•5 citations

Neural Network Based Black-box Estimator of Mutual Information

Tobias A. Eriksson, Diego Villafani, Henrik Åhlfeldt +1 more

We show that neural networks with softmax outputs are capable of estimating a lower bound on the mutual information of memoryless communication channels without prior knowledge of the channel properties.

Optica Advanced Photonics Congress•Jan 1, 2022•0 citations

Estimating quantum discord for two-qubit quantum systems via machine learning

Guo‐Zhu Pan, Junlong Zhao, Jian Zhou +2 more

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.

Physica Scripta•Sep 23, 2025•1 citation

A survey on quantum deep learning

Huaiguang Wu, Jiahui Zhang, Lijie Wang +3 more

The Journal of Supercomputing•Mar 3, 2025•13 citations

Neural Network Statistical Mechanics

Lingxiao Wang, Yin Jiang, Kai Zhou

We propose a general framework to extract microscopic interactions from raw configurations with deep neural networks. The approach replaces the modeling Hamiltonian by the neural networks, in which the interaction is encoded. It can be trained with data collected from Ab initio computations or experiments. The well-trained neural networks give an accurate estimation of the possibility distribution of the configurations at fixed external parameters. It can be spontaneously extrapolated to detect the phase structures since classical statistical mechanics as prior knowledge here. We apply the approach to a 2D spin system, training at a fixed temperature, and reproducing the phase structure. Scaling the configuration on lattice exhibits the interaction changes with the degree of freedom, which can be naturally applied to the experimental measurements. Our approach bridges the gap between the real configurations and the microscopic dynamics with an autoregressive neural network.

arXiv:2007.01037•Jul 2, 2020•2 citations

A Review of Quantum Neural Networks

Mohammad R. Khosravi, Maryam Zekri

•Dec 10, 2012•0 citations
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.
We propose a general framework to extract microscopic interactions from raw configurations with deep neural networks. The approach replaces the modeling Hamiltonian by the neural networks, in which the interaction is encoded. It can be trained with data collected from Ab initio computations or experiments. The well-trained neural networks give an accurate estimation of the possibility distribution of the configurations at fixed external parameters. It can be spontaneously extrapolated to detect the phase structures since classical statistical mechanics as prior knowledge here. We apply the approach to a 2D spin system, training at a fixed temperature, and reproducing the phase structure. Scaling the configuration on lattice exhibits the interaction changes with the degree of freedom, which can be naturally applied to the experimental measurements. Our approach bridges the gap between the real configurations and the microscopic dynamics with an autoregressive neural network.