QFlow Triangles: Quantum dot triangle plots data for machine learning

Arrays of QDs--interconnected islands of electrons confined in a semiconductor heterostructure with unique properties that allow them to act as artificial atoms--are a leading candidate for use as qubits, the fundamental information carriers in quantum computers. Scaling these systems to large arrays suitable for quantum computations is challenging. As the number of QDs grows, the number of gates needed to control them grows, making the manual tuning process unfeasible. Establishing a stable configuration of electrons in space is a non-trivial task achieved via electrostatic confinement, band-gap engineering, and dynamically adjusted voltages on nearby electrical gates. A key task is to determine a good set of control parameters (gate voltages) to achieve a desired charge configuration--in terms of number, location, and connectivity--for a successful experiment.

One sub-problem within the tuning procedure is determining the voltage placement of gates to allow the formation of isolated one-dimensional (1D) current channels inside the two-dimensional electron gas (2DEG) formed at the intersection of the Si and Si$_{x}$Ge$_{1-x}$ layers in the heterostructure. The 1D current channels are formed by selectively removing 2DEG from certain regions of the device. The formation of the channel manifests as a triangular-sloped region (the so-called triangle plots).