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دسته بندی:
محاسبات کوانتومی - Quantum-Computing
سال انتشار:
2022
عنوان انگلیسی مقاله:
Learning Quantum Drift-Diffusion Phenomenon by Physics-Constraint Machine Learning
ترجمه فارسی عنوان مقاله:
یادگیری پدیده رانش کوانتومی- انتشار با یادگیری ماشین محدودیت فیزیک
منبع:
ieee - ieee/ACM Transactions on Networking;2022;30;5;10:1109/TNET:2022:3158987
نویسنده:
Chun Li; Yunyun Yang; Hui Liang; Boying Wu
چکیده انگلیسی:
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 equationquantum Navier-Stokes equation.
Index Terms: Quantum machine learning | quantum drift diffusion | physical-information learning | quantum transport | quantum fluid model.
قیمت: رایگان
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