Systematically tuning 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 sur-face 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. By extending the number of quantum dots increases the complexity of the calibration process, which becomes impractical to do heuristically. Inspired by recent demonstrations of industry-fabricated silicon quantum dot bilinear arrays, we develop a theoretical framework to tune a 2×N 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 from a stability diagram and test the algorithm on simulated and experimental data of a 2×2 quantum dot array.