



02275nas a2200193   4500008004100000245007200041210006900113260001500182490000600197520168200203100002401885700002101909700002601930700002301956700002301979700002302002700001902025856003702044       2021                        eng d00aRay-based framework for state identification in quantum dot devices0 aRaybased framework for state identification in quantum dot devic  c06/17/20210 v23 a<p>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.</p>1 aZwolak, Justyna, P.1 aMcJunkin, Thomas1 aKalantre, Sandesh, S.1 aNeyens, Samuel, F.1 aMacQuarrie, E., R.1 aEriksson, Mark, A.1 aTaylor, J., M.  uhttps://arxiv.org/abs/2102.1178401423nas a2200157   4500008004100000245006500041210006300106260001400169520093400183100002401117700002601141700002101167700002101188700001901209856003701228       2020                        eng d00aRay-based classification framework for high-dimensional data0 aRaybased classification framework for highdimensional data  c10/1/20203 a<p>While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than using dense, multi-dimensional data, we propose a deep neural network (DNN) classification framework that utilizes a minimal collection of one-dimensional representations, called \emph{rays}, to construct the &quot;fingerprint&quot; of the structure(s) based on substantially reduced information. We empirically study this framework using a synthetic dataset of double and triple quantum dot devices and apply it to the classification problem of identifying the device state. We show that the performance of the ray-based classifier is already on par with traditional 2D images for low dimensional systems, while significantly cutting down the data acquisition cost.</p>1 aZwolak, Justyna, P.1 aKalantre, Sandesh, S.1 aMcJunkin, Thomas1 aWeber, Brian, J.1 aTaylor, J., M.  uhttps://arxiv.org/abs/2010.00500