Neural-network-designed pulse sequences for robust control of singlet-triplet qubits

Yang, Xu-Chen1,2; Yung, Man-Hong3,4,5; Wang, Xin1,2

2018-04-16

10.1103/PhysRevA.97.042324

PHYSICAL REVIEW A   影响因子和分区

2469-9926

2469-9934

Composite pulses are essential for universal manipulation of singlet-triplet spin qubits. In the absence of noise, they are required to perform arbitrary single-qubit operations due to the special control constraint of a singlet-triplet qubit, while in a noisy environment, more complicated sequences have been developed to dynamically correct the error. Tailoring these sequences typically requires numerically solving a set of nonlinear equations. Here we demonstrate that these pulse sequences can be generated by a well-trained, double-layer neural network. For sequences designed for the noise-free case, the trained neural network is capable of producing almost exactly the same pulses known in the literature. For more complicated noise-correcting sequences, the neural network produces pulses with slightly different line shapes, but the robustness against noises remains comparable. These results indicate that the neural network can be a judicious and powerful alternative to existing techniques in developing pulse sequences for universal fault-tolerant quantum computation.

SCI ; EI

英语

其他

Science, Technology and Innovation Commission of Shenzhen Municipality[ZDSYS20170303165926217] ; Science, Technology and Innovation Commission of Shenzhen Municipality[JCYJ20170412152620376]

Optics ; Physics

Optics ; Physics, Atomic, Molecular & Chemical

WOS:000430054000001

AMER PHYSICAL SOC

20181705036896

Nonlinear equations ; Quantum computers ; Robust control

Computer Systems and Equipment:722 ; Computer Software, Data Handling and Applications:723 ; Automatic Control Principles and Applications:731

PHYSICS

Web of Science

1.City Univ Hong Kong, Dept Phys, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China
2.City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
5.Shenzhen Key Lab Quantum Sci & Engn, Shenzhen 518055, Peoples R China

Yang, Xu-Chen,Yung, Man-Hong,Wang, Xin. Neural-network-designed pulse sequences for robust control of singlet-triplet qubits[J]. PHYSICAL REVIEW A,2018,97(4).

Yang, Xu-Chen,Yung, Man-Hong,&Wang, Xin.(2018).Neural-network-designed pulse sequences for robust control of singlet-triplet qubits.PHYSICAL REVIEW A,97(4).

Yang, Xu-Chen,et al."Neural-network-designed pulse sequences for robust control of singlet-triplet qubits".PHYSICAL REVIEW A 97.4(2018).