01798nas a2200133 4500008004100000245007100041210006900112260001400181490000700195520137900202100002401581700002201605856003701627 2023 eng d00aColloquium: Advances in automation of quantum dot devices control0 aColloquium Advances in automation of quantum dot devices control c2/17/20230 v953 a
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.
1 aZwolak, Justyna, P.1 aTaylor, Jacob, M. uhttps://arxiv.org/abs/2112.0936201850nas a2200337 4500008004100000245008100041210006900122260001500191520083900206100002401045700002201069700001801091700001801109700002001127700002301147700002301170700002301193700002301216700002301239700002501262700001801287700002201305700002401327700001701351700002501368700002101393700002301414700002101437700001701458856003701475 2023 eng d00aData Needs and Challenges of Quantum Dot Devices Automation: Workshop Report0 aData Needs and Challenges of Quantum Dot Devices Automation Work c12/21/20233 aGate-defined quantum dots are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. In this report, we outline current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present ideas put forward by the quantum dot community on how to overcome them.
1 aZwolak, Justyna, P.1 aTaylor, Jacob, M.1 aAndrews, Reed1 aBenson, Jared1 aBryant, Garnett1 aButerakos, Donovan1 aChatterjee, Anasua1 aSarma, Sankar, Das1 aEriksson, Mark, A.1 aGreplová, Eliška1 aGullans, Michael, J.1 aHader, Fabian1 aKovach, Tyler, J.1 aMundada, Pranav, S.1 aRamsey, Mick1 aRasmussen, Torbjoern1 aSeverin, Brandon1 aSigillito, Anthony1 aUndseth, Brennan1 aWeber, Brian uhttps://arxiv.org/abs/2312.1432201635nas a2200181 4500008004100000245010500041210006900146260001500215300001200230490000600242520106000248100001801308700002001326700002501346700002101371700002401392856003701416 2022 eng d00aCombining machine learning with physics: A framework for tracking and sorting multiple dark solitons0 aCombining machine learning with physics A framework for tracking c06/01/2022 a023163 0 v43 aIn ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, SolDet, is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.
1 aGuo, Shangjie1 aKoh, Sophia, M.1 aFritsch, Amilson, R.1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2111.0488101358nas a2200157 4500008004100000245008900041210006900130260001500199520084100214100002501055700001801080700002001098700002101118700002401139856003701163 2022 eng d00aDark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research0 aDark Solitons in BoseEinstein Condensates A Dataset for Manybody c05/17/20223 aWe establish a dataset of over 1.6×104 experimental images of Bose-Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and object detector as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.
1 aFritsch, Amilson, R.1 aGuo, Shangjie1 aKoh, Sophia, M.1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2205.0911401763nas a2200169 4500008004100000245007700041210006900118260001500187490000600202520123700208100002101445700002601466700002101492700001901513700002401532856003701556 2022 eng d00aTheoretical bounds on data requirements for the ray-based classification0 aTheoretical bounds on data requirements for the raybased classif c02/26/20220 v33 aThe 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'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.
1 aWeber, Brian, J.1 aKalantre, Sandesh, S.1 aMcJunkin, Thomas1 aTaylor, J., M.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2103.0957701880nas a2200229 4500008004100000245005800041210005800099260001500157490000700172520123000179100002001409700002101429700001701450700002601467700002001493700001701513700001801530700001901548700002201567700002401589856003701613 2022 eng d00aToward Robust Autotuning of Noisy Quantum dot Devices0 aToward Robust Autotuning of Noisy Quantum dot Devices c02/26/20220 v173 aThe 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.
1 aZiegler, Joshua1 aMcJunkin, Thomas1 aJoseph, E.S.1 aKalantre, Sandesh, S.1 aHarpt, Benjamin1 aSavage, D.E.1 aLagally, M.G.1 aEriksson, M.A.1 aTaylor, Jacob, M.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2108.0004301499nas a2200181 4500008004100000245008200041210006900123260001400192300001100206490000600217520094800223100001801171700002501189700002101214700002101235700002401256856003701280 2021 eng d00aMachine-learning enhanced dark soliton detection in Bose-Einstein condensates0 aMachinelearning enhanced dark soliton detection in BoseEinstein c6/17/2021 a0350200 v23 aMost data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons -- appearing as local density depletions in a BEC -- using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic Bose-Einstein condensates (BECs) utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.
1 aGuo, Shangjie1 aFritsch, Amilson, R.1 aGreenberg, Craig1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2101.0540402275nas 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 aQuantum 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.
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/2102.1178402481nas a2200229 4500008004100000245006800041210006700109260001300176490000700189520180000196100002401996700002102020700002602041700001902067700002302086700001902109700002002128700002402148700002302172700001902195856003702214 2020 eng d00aAuto-tuning of double dot devices in situ with machine learning0 aAutotuning of double dot devices in situ with machine learning c4/1/20200 v133 aThere 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.
1 aZwolak, Justyna, P.1 aMcJunkin, Thomas1 aKalantre, Sandesh, S.1 aDodson, J., P.1 aMacQuarrie, E., R.1 aSavage, D., E.1 aLagally, M., G.1 aCoppersmith, S., N.1 aEriksson, Mark, A.1 aTaylor, J., M. uhttps://arxiv.org/abs/1909.0803001423nas a2200157 4500008004100000245006500041210006300106260001400169520093400183100002401117700002601141700002101167700002101188700001901209856003701228 2020 eng d00aRay-based classification framework for high-dimensional data0 aRaybased classification framework for highdimensional data c10/1/20203 aWhile 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.
1 aZwolak, Justyna, P.1 aKalantre, Sandesh, S.1 aMcJunkin, Thomas1 aWeber, Brian, J.1 aTaylor, J., M. uhttps://arxiv.org/abs/2010.0050002126nas a2200109 4500008004100000245010900041210006900150520172200219100001401941700002401955856003701979 2018 eng d00aPractitioner's guide to social network analysis: Examining physics anxiety in an active-learning setting0 aPractitioners guide to social network analysis Examining physics3 aThe application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner's guide to carrying out research using SNA, including how to develop data collection instruments, set up protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students' physics anxiety and the social networks they participate in throughout the course of a semester. We find that students' with greater numbers of outgoing interactions are more likely to experience negative anxiety shifts even while we control for {\it pre} anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for letting other researchers use this approach in their work -- the {\it SNA toolbox} -- that can be accessed on GitHub.
1 aDou, Remy1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/1809.0033702679nas a2200181 4500008004100000245010000041210006900141260000900210300001300219490000700232520211600239100002402355700002602379700001602405700002002421700001902441856003702460 2018 eng d00aQFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments0 aQFlow lite dataset A machinelearning approach to the charge stat c2018 ae02058440 v133 aOver the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner's accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts
1 aZwolak, Justyna, P.1 aKalantre, Sandesh, S.1 aWu, Xingyao1 aRagole, Stephen1 aTaylor, J., M. uhttps://arxiv.org/abs/1809.1001801846nas a2200169 4500008004100000245006600041210006500107520131000172100001701482700001801499700002801517700002501545700002201570700002301592700002401615856003701639 2018 eng d00aStudying community development: a network analytical approach0 aStudying community development a network analytical approach3 aResearch shows that community plays a central role in learning, and strong community engages students and aids in student persistence. Thus, understanding the function and structure of communities in learning environments is essential to education. We use social network analysis to explore the community dynamics of students in a pre-matriculation, two-week summer program. Unlike previous network analysis studies in PER, we build our networks from classroom video that has been coded for student interactions using labeled, directed ties. We define 3 types of interaction: on task interactions (regarding the assigned task), on topic interactions (having to do with science, technology, engineering, and mathematics (STEM)), and off topic interactions (unrelated to the assignment or STEM). To study the development of community in this program, we analyze the shift in conversation topicality over the course of the program. Conversations are more on-task toward the end of the program and we propose that this conversational shift represents a change in student membership within their forming community.
1 aHass, C., A.1 aGenz, Florian1 aKustusch, Mary, Bridget1 aOuime, Pierre-P., A.1 aPomian, Katarzyna1 aSayre, Eleanor, C.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/1808.0819302365nas a2200181 4500008004100000245008600041210006900127260001500196520178600211100002601997700002402023700002002047700001602067700002402083700002002107700001902127856003702146 2017 eng d00aMachine Learning techniques for state recognition and auto-tuning in quantum dots0 aMachine Learning techniques for state recognition and autotuning c2017/12/133 aRecent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many cases, including in quantum-dot based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification using so-called deep neural networks have shown surprising successes for computer-aided understanding of complex systems. In this work, we use deep and convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when one can only measure a current-voltage characteristic of transport (here conductance) through such a device. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas-Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 % accuracy for charge and state identification for single and double dots purely from the dependence of the nanowire’s conductance upon gate voltages. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call ‘auto-tuning’. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental data set, and outline further problems in this domain, from using charge sensing data to extensions to full one and two-dimensional arrays, that can be tackled with machine learning.
1 aKalantre, Sandesh, S.1 aZwolak, Justyna, P.1 aRagole, Stephen1 aWu, Xingyao1 aZimmerman, Neil, M.1 aStewart, M., D.1 aTaylor, J., M. uhttps://arxiv.org/abs/1712.04914