Automated tuning of double quantum dots into specific charge states using neural networks

Abstract

While quantum dots are at the forefront of quantum device technology, tuning multi-dot 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 towards 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 pre-selected 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.

Machine learning enables completely automatic tuning of a quantum device faster than human experts

“…Moreover, the transport features that indicate the device is tuned to the double dot regime are time-consuming to measure and difficult to parametrise. Machine learning techniques and other automated approaches have been used for tuning quantum devices [5][6][7][8][9][10][11][12][13]. These techniques are limited to small regions of the device parameter space or require information about the device characteristics.…”

Quantum device fine-tuning using unsupervised embedding learning

“…The second stage, known as coarse tuning, focuses on identifying and navigating different operating regimes of a quantum dot device. Automated coarse tuning has been demonstrated using convolutional neural networks to identify the double quantum dot regime [8] and reach arbitrary charge states [9]. Template matching was also used to navigate to the single-electron regime [10].…”