Tracking of entanglement evolution in non-Markovian environments: a machine learning approach

Tracking quantum entanglement information is vital for quantum information processing. Conventional quantum state tomography can recover entanglement dynamics but requires a large number of measurements, especially for multi-qubit and open system situations. In this paper, we propose using the Multilayer Perceptron (MLP) to interpolate entanglement dynamics from sparsely sampled data. We exemplify our approach using an analytical solvable model of two entangled qubits each coupled to a non-Markovian dephasing environment. We demonstrate that the MLP can efficiently track the entanglement dynamics based on a limited sample of data. This solvable model enables us to evaluate the performance of MLP approach across different sampling schemes, system parameters, and coupling configurations. The results indicate that the MLP provides an efficient and robust solution for entanglement tracking in non-Markovian open quantum systems.