دسته بندی:
محاسبات کوانتومی - Quantum-Computing
سال انتشار:
2022
عنوان انگلیسی مقاله:
A Distributed Learning Scheme for Variational Quantum Algorithms
ترجمه فارسی عنوان مقاله:
یک طرح یادگیری توزیع شده برای الگوریتم های کوانتومی متغیر
منبع:
ieee - ieee Transactions on Quantum Engineering;2022;3; ;10:1109/TQE:2022:3175267
نویسنده:
YUXUAN DU1 , YANG QIAN1,2, XINGYAO WU1, AND DACHENG TAO
چکیده انگلیسی:
Variational quantum algorithms (VQAs) are prime contenders to gain computational advantages over classical algorithms using near-term quantum machines. As such, many endeavors have been made
to accelerate the optimization of modern VQAs in past years. To further improve the capability of VQAs,
here, we propose a quantum distributed optimization scheme (dubbed as QUDIO), whose back ends support
both real quantum devices and various quantum simulators. Unlike traditional VQAs subsuming a single
quantum chip or simulator, QUDIO collaborates with multiple quantum machines or simulators to complete
learning tasks. In doing so, the required wall-clock time for optimization can be continuously reduced by
increasing the accessible computational resources when ignoring the communication and synchronization
time. Moreover, through the lens of optimization theory, we unveil the potential factors that could affect
the convergence of QUDIO. In addition, we systematically understand the ability of QUDIO to reduce
wall-clock time via two standard benchmarks, which are hand-written image classification and the ground
energy estimation of the dihydrogen. Our proposal facilitates the development of advanced VQAs to narrow
the gap between the state of the art and applications with the quantum advantage.
INDEX TERMS: Distributed optimization | quantum computing | quantum Hamiltonians | quantum machine learning.
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0