Algorithm for automated tuning of a quantum dot into the single-electron regime

Lapointe-Major, M.; Germain, Olivier; Lemyre, Julien Camirand; Lachance-Quirion, Dany; Rochette, Sophie; Lemyre, Félix Camirand; Pioro‐Ladrière, Michel

doi:10.1103/physrevb.102.085301

Cited by 20 publications

(24 citation statements)

References 26 publications

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“…The current I SET can be measured as a function of the voltages at the QD plunger gate (V 1 applied at gate G1) and the electron reservoir gate (V 2 applied at gate G2), while keeping all other gate voltages fixed, providing two-dimensional charge stability diagrams [2]. Transitions between different charge states appear as distinct lines in the stability diagram and must be identified in order to tune the QD [11,15,26,34].…”

Section: A Data: Experimental and Syntheticmentioning

confidence: 99%

“…In autotuning of QDs, one of the simplest control tasks involves the identification of charge state transition lines in two-dimensional stability diagrams. While previous works use image analysis algorithms [26] or deep (convolutional) neural networks [11,15], in this paper we explore whether this task can be performed by extremely small feed-forward neural networks. Mixed-signal vector-matrix multiplication accelerators based on crossbar arrays of emerging memory technologies (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Miniaturizing neural networks for charge state autotuning in quantum dots

Czischek,

Yon,

Genest

et al. 2021

Preprint

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Section: A Data: Experimental and Syntheticmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Miniaturizing neural networks for charge state autotuning in quantum dots

Czischek,

Yon,

Genest

et al. 2021

Preprint

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“…There are also methods to achieve a specific number of electrons in each dot [22], or measure and modify the couplings in multi-dot systems [23,24]. These various automation techniques have utilized many different tools: convolutional neural networks (CNNs) [21,25], deep generative modeling [26], classical feature extraction, e.g., a Hough transformation [23,27], and many custom fitting models [28].…”

Section: Introductionmentioning

confidence: 99%

Ray-based framework for state identification in quantum dot devices

Zwolak,

McJunkin,

Kalantre

et al. 2021

Preprint

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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.

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“…This is fast enough for live stablity diagram measurement, which significantly improves the throughput of manual tuning of QD parameters. The automated sensor tuning in the hardware loop also allows for acquisition of unmistakable charge stability diagrams that are readily used as the input data for software-automated tuning of quantum dot arrays [18][19][20][21][22]. Furthermore, we demonstrate the application of the feedback control to single-shot spin readout synchronized with qubit control pulses.…”

mentioning

confidence: 99%

Real-Time Feedback Control of Charge Sensing for Quantum Dot Qubits

et al. 2021

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Measurement of charge configurations in few-electron quantum dots is a vital technique for spinbased quantum information processing. While fast and high-fidelity measurement is possible by using proximal quantum dot charge sensors, their operating range is limited and prone to electrical disturbances. Here we demonstrate realtime operation of a charge sensor in a feedback loop to maintain its sensitivity suitable for fast charge sensing in a Si/SiGe double quantum dot. Disturbances to the charge sensitivity, due to variation of gate voltages for operating the quantum dot and 1/f charge fluctuation, are compensated by a digital PID controller with the bandwidth of ≈ 100 kHz. The rapid automated tuning of a charge sensor enables unobstructed charge stability diagram measurement facilitating realtime quantum dot tuning and submicrosecond single-shot spin readout without compromising the performance of a charge sensor in time-consuming experiments for quantum information processing.

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Algorithm for automated tuning of a quantum dot into the single-electron regime

Cited by 20 publications

References 26 publications

Miniaturizing neural networks for charge state autotuning in quantum dots

Miniaturizing neural networks for charge state autotuning in quantum dots

Ray-based framework for state identification in quantum dot devices

Real-Time Feedback Control of Charge Sensing for Quantum Dot Qubits

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