Transformer models and end-to-end learning frameworks are rapidly revolutionizing the field of artificial intelligence. In this work, we apply object detection transformers to analyze charge stability diagrams in semiconductor quantum dot arrays, a key task for achieving scalability with spin-based quantum computing. Specifically, our model identifies triple points and their connectivity, which is crucial for virtual gate calibration, charge state initialization, drift correction, and pulse sequencing. We show that it surpasses convolutional neural networks in performance on three different spin qubit architectures, all without the need for retraining. In contrast to existing approaches, our method significantly reduces complexity and runtime, while enhancing generalizability. The results highlight the potential of transformer-based end-to-end learning frameworks as a foundation for a scalable, device- and architecture-agnostic tool for control and tuning of quantum dot devices.

Breakthroughs, discoveries, and DIY tips sent every weekday. Much like mermaids, the kraken, or the hafgufa, rogue waves have been regarded as a maritime myth. These waves do not always leave a lot of data behind, making it feel as if they spring up from the depths out of nowhere. However, one monster wave did leave data behind for scientists. On January 1, 1995, a monstrous 80-foot wave slammed into the Draupner oil platform in the North Sea.

Loredana Paglialonga leans across her father's prone body and whispers in his ear: "Spinta, Papi, spinta" ("Push Daddy, push"). It is impossible to tell whether Anselmo Paglialonga, a former major in the Italian carabinieri, has heard. Paralysed from head to toe with amyotrophic lateral sclerosis (ALS), Anselmo is completely "locked in". Unable to speak or open his eyes, his only hope of communicating is via a state-of-the-art neuroheadset attached to his scalp. Designed by Emotiv Systems, a Californian neuroengineering company, the Epoc headset purports to give users the power to control objects with their thoughts and was a succès fou on Kickstarter, where users stumped up $1.6m to fund its development – 16 times Emotiv's original target. Indeed right now, I am told, the 14 marble-sized electrodes in the Epoc's plastic clip-on frame are monitoring the EEG signals from Anselmo's brain and sending them wirelessly to a control unit. By analysing those signals using a machine-learning algorithm, BrainControl, a Sienna-based developer, claims to be able to distinguish Anselmo's thoughts and intentions from other brain noise and use those signals to operate a cursor on a tablet computer.