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Learning Quantum Drift-Diffusion Phenomenon by Physics-Constraint Machine Learning
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DOI：10.1109/TNET.2022.3158987.png)
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Abstract
En 中文
Recently, deep learning (DL) is widely used to detect physical phenomena and has obtained encouraging results. Several works have shown that it can learn quantum phenomenon. Subsequently, quantum machine learning (QML) has been paid more attention by academia and industry. Quantum drift-diffusion (QDD) is a commonplace physical phenomenon, which is a macroscopic description of electrons and holes in a semiconductor. They are commonly used to attain an understanding of the property of semiconductor devices in physics and engineering. We are motivated by the relaxation-time limit from the quantum-Navier-Stokes-Poisson system (QNSP) to the QDD equation and the existence of finite energy weak solutions to the QDD equation has been proved. Therefore, in this work, the quantum drift-diffusion learning neural network (QDDLNN) is proposed to investigate the quantum drift phenomena from limited observations. Furthermore, a piece of numerical evidence is found that the NNs can describe quantum transport phenomena by simulating the quantum confinement transport equation-quantum Navier-Stokes equation.
Keywords:
Mathematical models
Quantum computing
Machine learning
Quantum mechanics
Machine learning algorithms
Numerical models
Semiconductor device modeling
Quantum machine learning
quantum drift-diffusion
physical-information learning
quantum transport
quantum fluid model
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4.4K
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