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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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. This study provides insight to aid in the practical application of different closed-loop learning algorithms in realistic physical scenarios.

Original languageEnglish (US)
Article number062605
JournalPhysical Review A
Volume102
Issue number6
DOIs
StatePublished - Dec 7 2020

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

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