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 ...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.展开更多
基金supported by the National Program on Key Basic Research Project of China(2021YFA1400900)the National Natural Science Foundation of China(11934002,12075128,and T2225008)+2 种基金Shanghai Municipal Science and Technology Major Project(2019SHZDZX01)Shanghai Science Foundation(21QA1400500)Shanghai Qi Zhi Institute。
基金This work was supported by the National Program on Key Basic Research Project of China(Grant Nos.2021YFA1400900 and 2017YFA0304204)the National Natural Science Foundation of China(Grant Nos.11774067 and 11934002)+3 种基金Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)Shanghai Science Foundation(Grant No.19ZR1471500)the Open Project of Shenzhen Institute of Quantum Science and Engineering(Grant No.SIQSE202002)X.Q.acknowledges support from the National Postdoctoral Program for Innovative Talents of China under Grant No.BX20190083.
文摘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.
