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Using experiment and modeling, we show that the data set generated when a learning algorithm is used to optimize a quantum system can help to uncover the physics behind the process being optimized. In particular, by optimizing the process of high-harmonic generation using shaped light pulses, we generate a large data set and analyze its statistical behavior. This behavior is then compared with theoretical predictions, verifying our understanding of the attosecond dynamics of high-harmonic generation and uncovering an anomalous region of parameter space.

Learning from learning algorithms: Application to attosecond dynamics of high-harmonic generation