Effective detection of quantum discord by using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2401.07405v2
• Date: Mon, 22 Jan 2024 00:23:02 GMT
• Title: Effective detection of quantum discord by using Convolutional Neural Networks
• Authors: Narjes Taghadomi, Azam Mani, Ali Fahim, Ali Bakoui, Mohammad Sadegh Salami
• Abstract summary: We design a Convolutional Neural Network (CNN) that uses 16 kernels to completely distinguish between discordant and non-discordant general two-qubit states. A Branching Convolutional Neural Network (BCNN) that can effectively detect quantum discord achieves an accuracy of around 85% or 99%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum discord is a form of correlation that is defined as the difference between quantum and classical mutual information of two parties. Due to the optimization involved in the definition of classical mutual information of quantum systems, calculating and distinguishing between discordant and non-discordant states is not a trivial task. Additionally, complete tomography of a quantum state is the prerequisite for the calculation of its quantum discord, and it is indeed resource consuming. Here, by using the relation between the kernels of the convolutional layers of an artificial neural network and the expectation value of operators in quantum mechanical measurements, we design a Convolutional Neural Network (CNN) that uses 16 kernels to completely distinguish between the discordant and non-discordant general two-qubit states. We have also designed a Branching Convolutional Neural Network (BCNN) that can effectively detect quantum discord. Our BCNN achieves an accuracy of around 85% or 99%, by utilizing only 5 or 8 kernels, respectively. Our results show that to detect the existence of quantum discord up to the desired accuracy, instead of complete tomography, one can use suitable quantum circuits to directly measure the expectation values of the kernels, and then a fully connected network will solve the detection problem.

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