Miniaturizing neural networks for charge state autotuning in quantum dots

Czischek, Stefanie; Yon, Victor; Genest, Marc-Antoine; Roux, Marc-Antoine; Rochette, Sophie; Lemyre, Julien Camirand; Moras, Mathieu; Pioro-Ladrière, Michel; Drouin, Dominique; Beilliard, Yann; Melko, Roger G.

doi:10.48550/arxiv.2101.03181

Cited by 2 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the NISQ era on the horizon [38], it is important to consider the practical aspect of implementing automated control as part of the device itself, in the "on-chip" fashion. The network architecture necessary for RBC is significantly simpler and smaller than for CNN classification, making it more suitable for an implementation on miniaturized hardware with low power consumption in the near future [39,40]. In particular, the neural network used to train the RBC comprises only four fully connected dense layers with 128, 64, 32, and 5 units, respectively.…”

Section: Discussionmentioning

confidence: 99%

Ray-based framework for state identification in quantum dot devices

Zwolak,

McJunkin,

Kalantre

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Ray-based framework for state identification in quantum dot devices

Zwolak,

McJunkin,

Kalantre

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We developed a physics-inspired simulator and introduced crossdevice validation to address this challenge. In the context of tuning quantum devices, deep learning has been used for various other tasks [17,19,[23][24][25][26][27], with some approaches using simulated data to train their algorithms [15,16,28].…”

Section: Introductionmentioning

confidence: 99%

Identifying Pauli spin blockade using deep learning

Schuff¹,

Lennon²,

Geyer³

et al. 2022

Preprint

View full text Add to dashboard Cite

Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.

show abstract

Miniaturizing neural networks for charge state autotuning in quantum dots

Cited by 2 publications

References 38 publications

Ray-based framework for state identification in quantum dot devices

Ray-based framework for state identification in quantum dot devices

Identifying Pauli spin blockade using deep learning

Contact Info

Product

Resources

About