Tuning Methods for Semiconductor Spin Qubits

Botzem, Tim; Shulman, Michael; Foletti, Sandra; Harvey, Shannon; Dial, Oliver; Bethke, Patrick; Cerfontaine, Pascal; McNeil, Robert; Mahalu, D.; Umansky, V.; Ludwig, Arne; Wieck, A. D.; Schuh, Dieter; Bougeard, Dominique; Yacoby, Amir; Bluhm, Hendrik

doi:10.1103/physrevapplied.10.054026

Cited by 49 publications

(47 citation statements)

References 59 publications

(56 reference statements)

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“…Moreover, the transport features that indicate the device is tuned as a double quantum dot are time-consuming to measure and difficult to parametrise. Machine learning techniques and other automated approaches have been used for tuning quantum devices [5][6][7][8][9][10][11][12][13][14] . These techniques are limited to small regions of the device parameter space or require information about the device characteristics.…”

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confidence: 99%

Machine learning enables completely automatic tuning of a quantum device faster than human experts

et al. 2020

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Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

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confidence: 99%

Machine learning enables completely automatic tuning of a quantum device faster than human experts

et al. 2020

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“…For interqubit capacitive coupling we consider two cases, taking E c = 0 as a lower and E c = 350 µeV as an upper bound. The upper bound is estimated from a typical charge stability diagram by determining the distance of the triple points belonging to the (1, 0) − (2, 1) transition [35].…”

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confidence: 99%

High-fidelity gate set for exchange-coupled singlet-triplet qubits

et al. 2020

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Qubit arrays with access to high-fidelity single-and two-qubit gates are a key ingredient for building a quantum computer. As semiconductor-based devices with several qubits become available, issues like residual interqubit coupling and additional constraints from scalable control hardware need to be tackled to retain the high gate fidelities demonstrated in single-qubit devices. Focusing on two exchange-coupled singlet-triplet spin qubits, we address these issues by considering realistic control hardware as well as Coulomb and interqubit exchange coupling that cannot be fully turned off. We use measured noise spectra for charge and magnetic field noise to numerically optimize experimentally realistic pulse sequences and show that two-qubit (single-qubit) gate fidelities of 99.90% (≥ 99.69%) can be reached in GaAs, while 99.99% (≥ 99.95%) can be achieved with vanishing magnetic field noise as in Si.

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“…Progress in quantum dots array sizes has been steady. Given that double and triple dots are being routinely used in experiments [17][18][19] and moderate linear array sizes (∼ 10 dots) [7], as well as two-dimensional arrays [6], are on the horizon, an auto-tuning procedure for these devices is a significant step for employing them in both the laboratories and in applications.…”

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confidence: 99%

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

Zwolak

Kalantre

et al. 2018

PLoS ONE

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BackgroundOver the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis 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 quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does 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 QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance.Materials and methodsTo 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. The gate voltages are the experimental ‘knobs’ for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks.Results and discussionFrom 200 training sets sampled randomly from the full dataset, we show that the learner’s accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, 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.

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Tuning Methods for Semiconductor Spin Qubits

Cited by 49 publications

References 59 publications

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Machine learning enables completely automatic tuning of a quantum device faster than human experts

High-fidelity gate set for exchange-coupled singlet-triplet qubits

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

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