  A key challenge in scaling quantum computers is the calibration and control
of multiple qubits. In solid-state quantum dots, the gate voltages required to
stabilize quantized charges are unique for each individual qubit, resulting in
a 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 been
successfully used for autotuning in the past. In this paper, we develop
extremely small feed-forward neural networks that can be used to detect
charge-state transitions in quantum dot stability diagrams. We demonstrate that
these neural networks can be trained on synthetic data produced by computer
simulations, and robustly transferred to the task of tuning an experimental
device into a desired charge state. The neural networks required for this task
are sufficiently small as to enable an implementation in existing memristor
crossbar arrays in the near future. This opens up the possibility of
miniaturizing powerful control elements on low-power hardware, a significant
step towards on-chip autotuning in future quantum dot computers.
