  Quantum dots (QDs) defined with electrostatic gates are a leading platform
for a scalable quantum computing implementation. However, with increasing
numbers of qubits, the complexity of the control parameter space also grows.
Traditional measurement techniques, relying on complete or near-complete
exploration via two-parameter scans (images) of the device response, quickly
become impractical with increasing numbers of gates. Here, we propose to
circumvent this challenge by introducing a measurement technique relying on
one-dimensional projections of the device response in the multi-dimensional
parameter space. Dubbed as the ray-based classification (RBC) framework, we use
this machine learning (ML) approach to implement a classifier for QD states,
enabling automated recognition of qubit-relevant parameter regimes. We show
that RBC surpasses the 82 % accuracy benchmark from the experimental
implementation of image-based classification techniques from prior work while
cutting down the number of measurement points needed by up to 70 %. The
reduction in measurement cost is a significant gain for time-intensive QD
measurements and is a step forward towards the scalability of these devices. We
also discuss how the RBC-based optimizer, which tunes the device to a
multi-qubit regime, performs when tuning in the two- and three-dimensional
parameter spaces defined by plunger and barrier gates that control the dots.
This work provides experimental validation of both efficient state
identification and optimization with ML techniques for non-traditional
measurements in quantum systems with high-dimensional parameter spaces and
time-intensive measurements.
