“…Machine learning (ML) has emerged as a promising tool for some of the experimental challenges with QD control [37]. Deep neural networks [39][40][41][42][43][44][45][46], image recognition [38,40,42,[47][48][49][50] and supervised classification [40,46,51] have been demonstrated to aid charge state characterization [41,42,48,49,51], coupling parameter tuning [37] and gate voltage optimization [36,41,43,46,49,52] in a single QD [43,49,51], double QDs [36-38, 42-44, 51], triple QDs and arrays of QDs [36,37,41,45,48]. Unsupervised statistical methods [52,53] and deterministic algorithms [36,[49][50][51] have also been used for double-QD tuning.…”