01846nas a2200337 4500008004100000245008100041210006900122260001500191520083900206100002401045700002201069700001801091700001801109700002001127700002301147700002301170700002301193700002301216700002301239700002101262700001801283700002201301700002401323700001701347700002501364700002101389700002301410700002101433700001701454856003701471 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 a

Gate-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, Michael1 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/2312.1432202275nas 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 a

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.

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 a

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.

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/1909.0803002892nas a2200397 4500008004100000245005600041210005500097260001500152520177700167100001701944700002301961700002101984700002202005700001902027700001602046700001902062700002502081700002302106700002002129700002002149700002602169700002302195700002402218700002202242700002302264700001602287700001302303700002002316700002402336700001702360700001902377700001802396700002302414700002002437856003702457 2019 eng d00aQuantum Simulators: Architectures and Opportunities0 aQuantum Simulators Architectures and Opportunities c12/14/20193 a

Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behaviors to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the NSF workshop on "Programmable Quantum Simulators," that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and education. 

1 aAltman, Ehud1 aBrown, Kenneth, R.1 aCarleo, Giuseppe1 aCarr, Lincoln, D.1 aDemler, Eugene1 aChin, Cheng1 aDeMarco, Brian1 aEconomou, Sophia, E.1 aEriksson, Mark, A.1 aFu, Kai-Mei, C.1 aGreiner, Markus1 aHazzard, Kaden, R. A.1 aHulet, Randall, G.1 aKollár, Alicia, J.1 aLev, Benjamin, L.1 aLukin, Mikhail, D.1 aMa, Ruichao1 aMi, Xiao1 aMisra, Shashank1 aMonroe, Christopher1 aMurch, Kater1 aNazario, Zaira1 aNi, Kang-Kuen1 aPotter, Andrew, C.1 aRoushan, Pedram uhttps://arxiv.org/abs/1912.06938