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A key challenge in scaling quantum computers is the calibration and controlof multiple qubits. In solid-state quantum dots, the gate voltages required tostabilize quantized charges are unique for each individual qubit, resulting ina high-dimensional control parameter space that must be tuned automatically.Machine learning techniques are capable of processing high-dimensional data -provided that an appropriate training set is available - and have beensuccessfully used for autotuning in the past. In this paper, we developextremely small feed-forward neural networks that can be used to detectcharge-state transitions in quantum dot stability diagrams. We demonstrate thatthese neural networks can be trained on synthetic data produced by computersimulations, and robustly transferred to the task of tuning an experimentaldevice into a desired charge state. The neural networks required for this taskare sufficiently small as to enable an implementation in existing memristorcrossbar arrays in the near future. This opens up the possibility ofminiaturizing powerful control elements on low-power hardware, a significantstep towards on-chip autotuning in future quantum dot computers.

Miniaturizing neural networks for charge state autotuning in quantum dots