典型文献
The reservoir learning power across quantum many-body localization transition
文献摘要：
Harnessing the quantum computation power of the present noisy-interme-diate-size-quantum devices has received tremendous interest in the last few years.Here we study the learning power of a one-dimensional long-range randomly-coupled quantum spin chain,within the framework of reser-voir computing.In time sequence learning tasks,we find the system in the quantum many-body localized(MBL)phase holds long-term memory,which can be attributed to the emergent local integrals of motion.On the other hand,MBL phase does not provide sufficient nonlinearity in learn-ing highly-nonlinear time sequences,which we show in a parity check task.This is reversed in the quantum ergodic phase,which provides sufficient nonlinearity but compromises memory capacity.In a complex learning task of Mackey-Glass prediction that requires both sufficient memory capacity and nonlinearity,we find optimal learning performance near the MBL-to-ergodic transition.This leads to a guiding principle of quantum reservoir engineering at the edge of quantum ergodicity reaching optimal learning power for generic complex reservoir learning tasks.Our theoretical finding can be tested with near-term NISQ quantum devices.
文献关键词：
中图分类号：
作者姓名：
Wei Xia;Jie Zou;Xingze Qiu;Xiaopeng Li
作者机构：
State Key Laboratory of Surface Physics,Institute of Nanoelectronics and Quantum Computing,and Department of Physics,Fudan University,Shanghai 200433,China;Shenzhen Institute for Quantum Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China;Shanghai Qi Zhi Institute,Al Tower,Xuhui District,Shanghai 200232,China
文献出处：
引用格式：
[1]Wei Xia;Jie Zou;Xingze Qiu;Xiaopeng Li-.The reservoir learning power across quantum many-body localization transition)[J].物理学前沿,2022(03):91-99
A类：
voir
B类：
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AB值：
0.528532
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