A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots (QDs), 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 QD 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 QD computers.

In order to restore a normal bladder function for patients with spinal-cord injuries, a very versatile eight-channel implantable urinary prosthesis is designed and implemented. The main application of the device is the stimulation of particular nerves in the sacral roots. The intensity, the format and the timing of the current pulses available at the channel outputs are easily programmable. Those parameters are received transdermally by the implanted device in 13-bit blocks. The information is either loaded into internal registers for immediate processing or stored in an external implantable memory for a deferred processing.