Toward Robust Autotuning of Noisy Quantum dot Devices

Ziegler, Joshua; McJunkin, Thomas; Joseph, Emily S.; Kalantre, Sandesh S.; Harpt, Benjamin; Savage, D. E.; Lagally, M.G.; Eriksson, M. A.; Zwolak, Justyna P.

doi:10.1103/physrevapplied.17.024069

Cited by 20 publications

(12 citation statements)

References 36 publications

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“…The role of the score function is to identify scans showing double dot features by scoring them highly whilst scoring other features, such as those from single dots, poorly. Score functions used in the literature to evaluate current measurements have been based on neural networks [8,34,35], Hough transforms [7,36], custom fitting models [6], handcrafted heuristics [5], and even ray-based classification [37]. These score functions are too noise-sensitive for fast measurements or cannot generalise to complex signals.…”

Section: Score Functionmentioning

confidence: 99%

All rf-based tuning algorithm for quantum devices using machine learning

Ares

Straaten

Fedele

et al. 2023

Preprint

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Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements, i.e. without resorting to current measurements. By exploiting their bandwidth and impedance matching, we demonstrate an algorithm that automatically tunes double quantum dots with only radio-frequency measurements. The tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.

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Section: Score Functionmentioning

confidence: 99%

All rf-based tuning algorithm for quantum devices using machine learning

Ares

Straaten

Fedele

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The role of the score function is to identify scans showing double dot features by scoring them highly whilst scoring other features, such as those from single dots, poorly. Score functions used in the literature to evaluate current measurements have been based on neural networks [7,32,33], Hough transforms [6,34], custom fitting models [5], handcrafted heuristics [4], and even ray-based classification [35]. These score functions are too noise-sensitive for fast measurements or cannot generalise to complex signals.…”

Section: Score Functionmentioning

confidence: 99%

All rf-based tuning algorithm for quantum devices using machine learning

Straaten¹,

Fedele²,

Vigneau³

et al. 2022

Preprint

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Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.

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“…Thus, the shape and orientation of the lines encodes sufficient qualitative information about the state of the device to enable state (in this case, charge topology) classification. As shown in [31], a CNN-based classifier trained for state identification learns to mask the noise captured between transition lines in these 2D charge sensing images.…”

Section: Ray-based Classification Frameworkmentioning

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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Toward Robust Autotuning of Noisy Quantum dot Devices

Cited by 20 publications

References 36 publications

All rf-based tuning algorithm for quantum devices using machine learning

All rf-based tuning algorithm for quantum devices using machine learning

All rf-based tuning algorithm for quantum devices using machine learning

Ray-based framework for state identification in quantum dot devices

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