Abstract
Quantum transport is a significant phenomenon in the mesoscopic system which is widely studied in recent years. In physical experiments, the fluctuation noise can reflect the microscopic properties of the quantum transport system easier than the transport current. In theory, the quantum conditional master equation (QCME) is a very effective method for studying quantum transport process of charge qubits. But, the QCME is an infinite recursive differential equation system that is difficult to solve theoretically. So, we try to solve this problem by combining the requirements of the solution and some advantages of the machine learning algorithms. Firstly, compared the calculation diagram of QCME and recurrent neural network (RNN), the relationship between them was found. Long Short-Term Memory (LSTM) has great advantages in processing time-series data as a typical network of RNN. Secondly, the behavior of parameters transfers from time \(t\) to \(t+\Delta t\) in QCME is consistent with the behavior of parameter transfer in LSTM. Based on the above two reasons, the LSTM is used to simulate QCME. In the numerical experiment, an effective method is proposed to truncate QCME, and then the fluctuation noise spectrum data from a two-level quantum transport system is used to train the LSTM network and calculate the transition probability of electron with the parameters of LSTM.
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Project supported by the National Key R&D Program of China, Grant No. 2018YFĂ703.
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Hu, Y., Li, XY., Zhu, QS. (2021). Study the Quantum Transport Process: Machine Learning Simulates Quantum Conditional Master Equation. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_12
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