NIST · ИТ · cc0b6e1f5072595a675e
Toward Robust Autotuning of Noisy Quantum Dot Devices
The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this
NIST · ИТОфициальный источник · 2022-02-25
{
"title_en": "Toward Robust Autotuning of Noisy Quantum Dot Devices",
"designation": "",
"series": "",
"status": "",
"date": "2022-02-25",
"date_original": "February 25, 2022",
"url": "https://www.nist.gov/publications/toward-robust-autotuning-noisy-quantum-dot-devices",
"source": "NIST Publications",
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"authors_en": "Joshua Ziegler, Thomas McJunkin , Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor , Justyna Zwolak",
"abstract_excerpt_en": "The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this",
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{
"title_en": "Toward Robust Autotuning of Noisy Quantum Dot Devices",
"designation": "",
"series": "",
"status": "",
"date": "2022-02-25",
"date_original": "February 25, 2022",
"url": "https://www.nist.gov/publications/toward-robust-autotuning-noisy-quantum-dot-devices",
"source": "NIST Publications",
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"authors_en": "Joshua Ziegler, Thomas McJunkin , Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor , Justyna Zwolak",
"abstract_excerpt_en": "The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this"
}
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