Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses

Yang, Xiao-dong; Arenz, Christian; Pelczer, Istvan; Chen, Qi-Ming; Wu, Re-Bing; Peng, Xinhua; Rabitz, Herschel

doi:10.1103/physreva.102.062605

Title: Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses

Journal Article · Mon Dec 07 00:00:00 EST 2020 · Physical Review A

DOI:https://doi.org/10.1103/physreva.102.062605· OSTI ID:1849397

^[1]; Arenz, Christian ^[2];

^[2]; Chen, Qi-Ming ^[2]; Wu, Re-Bing ^[3];

^[4];

^[2]

Univ. of Science and Technology of China, Hefei (China); Princeton Univ., NJ (United States)
Princeton Univ., NJ (United States)
Tsinghua Univ., Beijing (China)
Univ. of Science and Technology of China, Hefei (China)

Designing a high-quality control is crucial for reliable quantum computation. Among the existing approaches, closed-loop leaning control is an effective choice. Its efficiency depends on the learning algorithm employed, thus deserving algorithmic comparisons for its practical applications. Here we assess three representative learning algorithms, including GRadient Ascent Pulse Engineering (GRAPE), improved Nelder-Mead (NMplus), and Differential Evolution (DE), by searching for high-quality control pulses to prepare the Bell state. We first implement each algorithm experimentally in a nuclear magnetic resonance system and then conduct a numerical study considering the impact of some possible significant experimental uncertainties. The experiments report the successful preparation of the high-fidelity target state by the three algorithms, while NMplus converges fastest, and these results coincide with the numerical simulations when potential uncertainties are negligible. However, under certain significant uncertainties, these algorithms possess distinct performance with respect to their resulting precision and efficiency, and DE shows the best robustness. Finally, this study provides insight to aid in the practical application of different closed-loop learning algorithms in realistic physical scenarios.

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Research Organization:: Princeton Univ., NJ (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC); Anhui Initiative in Quantum Information Technologies; US Army Research Office (ARO)

Grant/Contract Number:: FG02-02ER15344; 2018YFA0306600; 11661161018; 11927811; AHY050000; W911NF-19-1-0382; 61833010; 61773232

OSTI ID:: 1849397

Journal Information:: Physical Review A, Vol. 102, Issue 6; ISSN 2469-9926

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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