@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 = {

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.

}, 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 = {

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 \"gatekeeper\" 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.

}, 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} } @article {2766, title = {Ray-based framework for state identification in quantum dot devices}, journal = {PRX Quantum}, volume = {2}, year = {2021}, month = {06/17/2021}, abstract = {

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.

}, doi = {https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.020335}, url = {https://arxiv.org/abs/2102.11784}, author = {Justyna P. Zwolak and Thomas McJunkin and Sandesh S. Kalantre and Samuel F. Neyens and E. R. MacQuarrie and Mark A. Eriksson and J. M. Taylor} } @article {2449, title = {Auto-tuning of double dot devices in situ with machine learning}, journal = {Phys. Rev. Applied }, volume = {13}, year = {2020}, month = {4/1/2020}, abstract = {

There are myriad quantum computing approaches, each having its own set of challenges to understand and effectively control their operation. Electrons confined in arrays of semiconductor nanostructures, called quantum dots (QDs), is one such approach. The easy access to control parameters, fast measurements, long qubit lifetimes, and the potential for scalability make QDs especially attractive. However, as the size of the QD array grows, so does the number of parameters needed for control and thus the tuning complexity. The current practice of manually tuning the qubits is a relatively time-consuming procedure and is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of an auto-tuning protocol proposed by Kalantre et al. [arXiv:1712.04914]. In particular, we discuss how to establish a seamless communication protocol between a machine learning (ML)-based auto-tuner and the experimental apparatus. We then show that a ML algorithm trained exclusively on synthetic data coming from a physical model to quantitatively classify the state of the QD device, combined with an optimization routine, can be used to replace manual tuning of gate voltages in devices. A success rate of over 85 \% is determined for tuning to a double quantum dot regime when at least one of the plunger gates is initiated sufficiently close to the desired state. Modifications to the training network, fitness function, and optimizer are discussed as a path towards further improvement in the success rate when starting both near and far detuned from the target double dot range.

}, doi = {https://doi.org/10.1103/PhysRevApplied.13.034075}, url = {https://arxiv.org/abs/1909.08030}, author = {Justyna P. Zwolak and Thomas McJunkin and Sandesh S. Kalantre and J. P. Dodson and E. R. MacQuarrie and D. E. Savage and M. G. Lagally and S. N. Coppersmith and Mark A. Eriksson and J. M. Taylor} } @article {2685, title = {Ray-based classification framework for high-dimensional data}, journal = {Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada}, year = {2020}, month = {10/1/2020}, abstract = {

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.

}, url = {https://arxiv.org/abs/2010.00500}, author = {Justyna P. Zwolak and Sandesh S. Kalantre and Thomas McJunkin and Brian J. Weber and J. M. Taylor} }