Effect of Weak Measurement Reversal on Quantum Correlations in a Correlated Amplitude Damping Channel, with a Neural Network Perspective

• URL: http://arxiv.org/abs/2506.05642v1
• Date: Fri, 06 Jun 2025 00:14:53 GMT
• Title: Effect of Weak Measurement Reversal on Quantum Correlations in a Correlated Amplitude Damping Channel, with a Neural Network Perspective
• Authors: Venkat Abhignan, Bidyut Bikash Boruah, R. Srikanth, Ashutosh Singh,
• Abstract summary: We study the evolution of quantum correlations in Bell, Werner, and maximally entangled mixed states of two qubits to correlated amplitude-damping channels.<n>We employ a neural network model to enhance our analysis of the relationship between different quantum correlation measures during the evolution.
• Score: 1.593690982728631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the evolution of quantum correlations in Bell, Werner, and maximally entangled mixed states of two qubits subjected to correlated amplitude-damping channels. Our primary focus is to evaluate the robustness of entanglement as a resource for quantum information protocols such as dense coding, teleportation, and Einstein-Podolsky-Rosen (EPR) steering under the influence of noise. In addition, we investigate the behaviour of other quantum correlations, including quantum discord and coherence, and analyze their hierarchy under decoherence. To counteract the detrimental effects of the channels, we apply the weak measurement and quantum measurement reversal (WMR) protocol, comparing the effectiveness of single-qubit and two-qubit WMR techniques. Our results show that the two-qubit WMR protocol significantly outperforms the single-qubit approach in preserving quantum correlations. Furthermore, we employ a neural network model to enhance our analysis of the relationship between different quantum correlation measures during the evolution. Using a MATLAB-based artificial neural network with 80 neurons across three hidden layers and trained with the Levenberg-Marquardt algorithm, we successfully predict trace distance discord from other correlations, achieving low prediction errors. Besides, our analysis of the neural network weights suggests that concurrence and EPR steering have the most positive influence on the accurate discord predictions.

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