Identifying Pauli spin blockade using deep learning

Schuff, Jonas; Lennon, Dominic T.; Geyer, Simon; Craig, David Latch; Fedele, Federico; Vigneau, Florian; Camenzind, Leon C.; Kuhlmann, Andreas V.; Briggs, G. Andrew D.; Zumbühl, Dominik M.; Sejdinović, Dino; Ares, Natalia

doi:10.22331/q-2023-08-08-1077

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…The time-consuming challenge of tuning semiconductor devices becomes intractable as we combine different device architectures in the realisation of complex quantum circuits with millions of components. The development of machine learning algorithms for quantum device tuning [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] is exceptionally challenging when looking for such overarching solutions, successful on very different types of devices which may need to cater to specific purposes.…”

Section: Introductionmentioning

confidence: 99%

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

Severin,

Lennon,

Camenzind

et al. 2024

Preprint

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The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

show abstract

Section: Introductionmentioning

confidence: 99%

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

Severin,

Lennon,

Camenzind

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…One of the reasons for this contrast is that a longstanding challenge remains: the intricate tuning required to reach and maintain qubit operation. Previous works have introduced diverse approaches for automating single stages of this process, such as defining double quantum dot (DQD) confinement potentials [9, 24-26], navigating to specific charge transitions [27][28][29][30][31][32][33][34], fine-tuning of charge transport features [35] or the inter-dot tunnel couplings [36,37], as well as device characterisation [38][39][40].…”

mentioning

confidence: 99%

Fully autonomous tuning of a spin qubit

Ares,

Schuff,

Carballido

et al. 2024

Preprint

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Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real `needle in the haystack' problem, which, until now, has resisted complete automation due to device variability and fabrication imperfections. In this study, we present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. We demonstrate this automation, achieved without human intervention, in a Ge/Si core/shell nanowire device. Our approach integrates deep learning, Bayesian optimization, and computer vision techniques. We expect this automation algorithm to apply to a wide range of semiconductor qubit devices, allowing for statistical studies of qubit quality metrics. As a demonstration of the potential of full automation, we characterise how the Rabi frequency and $g$-factor depend on barrier gate voltages for one of the qubits found by the algorithm. Twenty years after the initial demonstrations of spin qubit operation, this significant advancement is poised to finally catalyze the operation of large, previously unexplored quantum circuits.

show abstract

Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning

Severin,

Lennon,

Camenzind

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

show abstract

Identifying Pauli spin blockade using deep learning

Cited by 3 publications

References 43 publications

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

Fully autonomous tuning of a spin qubit

Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning

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