Ray-based framework for state identification in quantum dot devices
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor
Quantum dots (QDs) defined with electrostatic gates are one of the leading candidates for scaling up the number of qubits in quantum computing implementations. However, with increasing qubit number, the complexity of the control parameter space also grows. Traditional measurement techniques relying on complete exploration via two-dimensional scans of the device response quickly become impractical. 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. We show that RBC surpasses the 82 % accuracy benchmark from the experimental implementation of the image-based classification techniques 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 auto-tuner performs when tuning in two- and three-dimensional parameter space defined by plunger and barrier gates defining the dots. This work opens up an avenue towards efficient state identification with ML techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.
, McJunkin, T.
, Kalantre, S.
, Neyens, S.
, MacQuarrie, E.
, Eriksson, M.
and Taylor, J.
Ray-based framework for state identification in quantum dot devices, PRX Quantum, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931926
(Accessed July 31, 2021)