Neural-network-designed pulse sequences for robust control of singlet-Triplet qubits
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Journal / Publication | Physical Review A |
Volume | 97 |
Issue number | 4 |
Online published | 16 Apr 2018 |
Publication status | Published - Apr 2018 |
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Abstract
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.
Citation Format(s)
Neural-network-designed pulse sequences for robust control of singlet-Triplet qubits. / Yang, Xu-Chen; Yung, Man-Hong; Wang, Xin.
In: Physical Review A, Vol. 97, No. 4, 04.2018.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review