The reservoir learning power across quantum many-body localization transition

Xia, Wei1,2; Zou, Jie1,2; Qiu, Xingze1,2,3; Li, Xiaopeng1,2,4

2022-06-01

10.1007/s11467-022-1158-1

Frontiers of Physics   影响因子和分区

2095-0462

2095-0470

Harnessing the quantum computation power of the present noisy-intermediate-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 reservoir 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 learning 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.

quantum reservoir computing many-body localization quantum ergodic edge of quantum ergodici optimal learning power

SCI

英语

通讯

National Program on Key Basic Research Project of China["2021YFA1400900","2017YFA0304204"] ; National Natural Science Foundation of China[11774067,11934002] ; Shanghai Municipal Science and Technology Major Project[2019SHZDZX01] ; Shanghai Science Foundation[19ZR1471500] ; Open Project of Shenzhen Institute of Quantum Science and Engineering[SIQSE202002] ; National Postdoctoral Program for Innovative Talents of China[BX20190083]

Physics

Physics, Multidisciplinary

WOS:000778484400002

HIGHER EDUCATION PRESS

Web of Science

1.Fudan Univ, Inst Nanoelect & Quantum Comp, State Key Lab Surface Phys, Shanghai 200433, Peoples R China
2.Fudan Univ, Dept Phys, Shanghai 200433, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
4.Shanghai Qi Zhi Inst, Al Tower, Shanghai 200232, Peoples R China

Xia, Wei,Zou, Jie,Qiu, Xingze,et al. The reservoir learning power across quantum many-body localization transition[J]. Frontiers of Physics,2022,17(3).

Xia, Wei,Zou, Jie,Qiu, Xingze,&Li, Xiaopeng.(2022).The reservoir learning power across quantum many-body localization transition.Frontiers of Physics,17(3).

Xia, Wei,et al."The reservoir learning power across quantum many-body localization transition".Frontiers of Physics 17.3(2022).