Automatic virtual voltage extraction of a 2xN array of quantum dots with machine learning.

Spin qubits in quantum dots are a compelling platform for fault-tolerant quantum computing due to the potential to fabricate dense two-dimensional arrays with nearest neighbour couplings, a requirement to implement the surface code. However, due to the proximity of the surface gate electrodes cross-coupling capacitances can be substantial, making it difficult to control each quantum dot independently. Increasing the number of quantum dots increases the complexity of the calibration process, which becomes impractical to do heuristically. Inspired by recent demonstrations of industrial-grade silicon quantum dot bilinear arrays, we develop a theoretical framework to cancel the effect of cross-capacitances in a 2xN array of quantum dots, based on the gradients in gate voltage space of different charge transitions that can be measured in multiple two-dimensional charge stability diagrams. To automate the process, we successfully train aneural network to extract the gradients from a Hough transformation of a stability diagram and test the algorithm on simulated and experimental data of a 2x2 quantum dot array.