Learning quantum Hamiltonians from single-qubit measurements
In the Hamiltonian-based quantum dynamics, to estimate Hamiltonians from the measured data is a vital topic. In this work, we propose a recurrent neural network to learn the target Hamiltonians from the temporal records of single-qubit measurements, which does not require the ground states and only...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2021-06-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.3.023246 |
| _version_ | 1797211006396530688 |
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| author | Liangyu Che Chao Wei Yulei Huang Dafa Zhao Shunzhong Xue Xinfang Nie Jun Li Dawei Lu Tao Xin |
| author_facet | Liangyu Che Chao Wei Yulei Huang Dafa Zhao Shunzhong Xue Xinfang Nie Jun Li Dawei Lu Tao Xin |
| author_sort | Liangyu Che |
| collection | DOAJ |
| description | In the Hamiltonian-based quantum dynamics, to estimate Hamiltonians from the measured data is a vital topic. In this work, we propose a recurrent neural network to learn the target Hamiltonians from the temporal records of single-qubit measurements, which does not require the ground states and only measures single-qubit observables. It is applicable on both time-independent and time-dependent Hamiltonians and can simultaneously capture the magnitude and sign of Hamiltonian parameters. Taking the Hamiltonians with the nearest-neighbor interactions as numerical examples, we trained our recurrent neural networks to learn different types of Hamiltonians with high accuracy. The study also shows that our method has good robustness against the measurement noise and decoherence effect. Therefore, it has widespread applications in estimating the parameters of quantum devices and characterizing the Hamiltonian-based quantum dynamics. |
| first_indexed | 2024-04-24T10:19:37Z |
| format | Article |
| id | doaj.art-9a8601d438b34efe8c9bca670a43c091 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:19:37Z |
| publishDate | 2021-06-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-9a8601d438b34efe8c9bca670a43c0912024-04-12T17:11:09ZengAmerican Physical SocietyPhysical Review Research2643-15642021-06-013202324610.1103/PhysRevResearch.3.023246Learning quantum Hamiltonians from single-qubit measurementsLiangyu CheChao WeiYulei HuangDafa ZhaoShunzhong XueXinfang NieJun LiDawei LuTao XinIn the Hamiltonian-based quantum dynamics, to estimate Hamiltonians from the measured data is a vital topic. In this work, we propose a recurrent neural network to learn the target Hamiltonians from the temporal records of single-qubit measurements, which does not require the ground states and only measures single-qubit observables. It is applicable on both time-independent and time-dependent Hamiltonians and can simultaneously capture the magnitude and sign of Hamiltonian parameters. Taking the Hamiltonians with the nearest-neighbor interactions as numerical examples, we trained our recurrent neural networks to learn different types of Hamiltonians with high accuracy. The study also shows that our method has good robustness against the measurement noise and decoherence effect. Therefore, it has widespread applications in estimating the parameters of quantum devices and characterizing the Hamiltonian-based quantum dynamics.http://doi.org/10.1103/PhysRevResearch.3.023246 |
| spellingShingle | Liangyu Che Chao Wei Yulei Huang Dafa Zhao Shunzhong Xue Xinfang Nie Jun Li Dawei Lu Tao Xin Learning quantum Hamiltonians from single-qubit measurements Physical Review Research |
| title | Learning quantum Hamiltonians from single-qubit measurements |
| title_full | Learning quantum Hamiltonians from single-qubit measurements |
| title_fullStr | Learning quantum Hamiltonians from single-qubit measurements |
| title_full_unstemmed | Learning quantum Hamiltonians from single-qubit measurements |
| title_short | Learning quantum Hamiltonians from single-qubit measurements |
| title_sort | learning quantum hamiltonians from single qubit measurements |
| url | http://doi.org/10.1103/PhysRevResearch.3.023246 |
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