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Quantum dots (QDs) defined with electrostatic gates are a leading platformfor a scalable quantum computing implementation. However, with increasingnumbers of qubits, the complexity of the control parameter space also grows.Traditional measurement techniques, relying on complete or near-completeexploration via two-parameter scans (images) of the device response, quicklybecome impractical with increasing numbers of gates. Here we propose tocircumvent this challenge by introducing a measurement technique relying onone-dimensional projections of the device response in the multidimensionalparameter space. Dubbed the ``ray-based classification (RBC) framework,'' weuse this machine learning approach to implement a classifier for QD states,enabling automated recognition of qubit-relevant parameter regimes. We showthat RBC surpasses the 82 % accuracy benchmark from the experimentalimplementation of image-based classification techniques from prior work whilereducing the number of measurement points needed by up to 70 %. The reductionin measurement cost is a significant gain for time-intensive QD measurementsand is a step forward toward the scalability of these devices. We also discusshow the RBC-based optimizer, which tunes the device to a multiqubit regime,performs when tuning in the two-dimensional and three-dimensional parameterspaces defined by plunger and barrier gates that control the QDs.This workprovides experimental validation of both efficient state identification andoptimization with machine learning techniques for non-traditional measurementsin quantum systems with high-dimensional parameter spaces and time-intensivemeasurements.

Ray-based framework for state identification in quantum dot devices