2020
DOI: 10.1103/physrevb.102.085301
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Algorithm for automated tuning of a quantum dot into the single-electron regime

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Cited by 22 publications
(24 citation statements)
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“…The current I SET can be measured as a function of the voltages at the QD plunger gate (V 1 applied at gate G1) and the electron reservoir gate (V 2 applied at gate G2), while keeping all other gate voltages fixed, providing two-dimensional charge stability diagrams [2]. Transitions between different charge states appear as distinct lines in the stability diagram and must be identified in order to tune the QD [11,15,26,34].…”
Section: A Data: Experimental and Syntheticmentioning
confidence: 99%
See 1 more Smart Citation
“…The current I SET can be measured as a function of the voltages at the QD plunger gate (V 1 applied at gate G1) and the electron reservoir gate (V 2 applied at gate G2), while keeping all other gate voltages fixed, providing two-dimensional charge stability diagrams [2]. Transitions between different charge states appear as distinct lines in the stability diagram and must be identified in order to tune the QD [11,15,26,34].…”
Section: A Data: Experimental and Syntheticmentioning
confidence: 99%
“…In autotuning of QDs, one of the simplest control tasks involves the identification of charge state transition lines in two-dimensional stability diagrams. While previous works use image analysis algorithms [26] or deep (convolutional) neural networks [11,15], in this paper we explore whether this task can be performed by extremely small feed-forward neural networks. Mixed-signal vector-matrix multiplication accelerators based on crossbar arrays of emerging memory technologies (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…There are also methods to achieve a specific number of electrons in each dot [22], or measure and modify the couplings in multi-dot systems [23,24]. These various automation techniques have utilized many different tools: convolutional neural networks (CNNs) [21,25], deep generative modeling [26], classical feature extraction, e.g., a Hough transformation [23,27], and many custom fitting models [28].…”
Section: Introductionmentioning
confidence: 99%
“…This is fast enough for live stablity diagram measurement, which significantly improves the throughput of manual tuning of QD parameters. The automated sensor tuning in the hardware loop also allows for acquisition of unmistakable charge stability diagrams that are readily used as the input data for software-automated tuning of quantum dot arrays [18][19][20][21][22]. Furthermore, we demonstrate the application of the feedback control to single-shot spin readout synchronized with qubit control pulses.…”
mentioning
confidence: 99%