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

Understand

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.