



@article {2765,
	title = {Theoretical bounds on data requirements for the ray-based classification},
	journal = {SN Comput. Sci.},
	volume = {3},
	year = {2022},
	month = {02/26/2022},
	abstract = {<p>The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed in which the intersections of a set of one-dimensional representations, called rays, with the boundaries of the shape are used to identify the specific geometry. This ray-based classification (RBC) has been empirically verified using a synthetic dataset of two- and three-dimensional shapes [1] and, more recently, has also been validated experimentally [2]. Here, we establish a bound on the number of rays necessary for shape classification, defined by key angular metrics, for arbitrary convex shapes. For two dimensions, we derive a lower bound on the number of rays in terms of the shape\&$\#$39;s length, diameter, and exterior angles. For convex polytopes in R^N, we generalize this result to a similar bound given as a function of the dihedral angle and the geometrical parameters of polygonal faces. This result enables a different approach for estimating high-dimensional shapes using substantially fewer data elements than volumetric or surface-based approaches.</p>},
	doi = {https://doi.org/10.1007/s42979-021-00921-0},
	url = {https://arxiv.org/abs/2103.09577},
	author = {Brian J. Weber and Sandesh S. Kalantre and Thomas McJunkin and J. M. Taylor and Justyna P. Zwolak}
}
@article {3005,
	title = {Toward Robust Autotuning of Noisy Quantum dot Devices},
	journal = {Physical Review Applied},
	volume = {17},
	year = {2022},
	month = {02/26/2022},
	abstract = {<p>The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a \&quot;gatekeeper\&quot; system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) \% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.</p>},
	doi = {https://doi.org/10.1103/PhysRevApplied.17.024069},
	url = {https://arxiv.org/abs/2108.00043},
	author = {Joshua Ziegler and Thomas McJunkin and E.S. Joseph and Sandesh S. Kalantre and Benjamin Harpt and D.E. Savage and M.G. Lagally and M.A. Eriksson and Jacob M. Taylor and Justyna P. Zwolak}
}
