Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks

While quantum dots are at the forefront of quantum-device technology, the tuning of multidot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. This process becomes increasingly time-consuming and difficult to perform manually as the devices become more complex and the number of tuning parameters grows. In this work, we present a crucial step toward automated tuning of quantum-dot qubits. We introduce an algorithm driven by machine learning that uses a small number of coarse-grained measurements as its input and tunes the quantum-dot system into a preselected charge state. We train and test our algorithm on a GaAs double-quantum-dot device and we consistently arrive at the desired state or its immediate neighborhood.