TY - JOUR T1 - Ray-based classification framework for high-dimensional data JF - Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada Y1 - 2020 A1 - Justyna P. Zwolak A1 - Sandesh S. Kalantre A1 - Thomas McJunkin A1 - Brian J. Weber A1 - J. M. Taylor AB -

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 "fingerprint" 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.

UR - https://arxiv.org/abs/2010.00500 ER -