Abstract
AbstractVariability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Reference23 articles.
1. Vandersypen, L. M. K. et al. Interfacing spin qubits in quantum dots and donors—hot, dense and coherent. npj Quantum Inf. 3, 1–10 (2016).
2. Hensgens, T. et al. Quantum simulation of a fermi–hubbard model using a semiconductor quantum dot array. Nature 548, 70–73 (2017).
3. Petta, J. R. et al. Coherent manipulation of coupled electron spins in semiconductor quantum dots. Science 309, 2180–2184 (2005).
4. Malinowski, F. K. et al. Notch filtering the nuclear environment of a spin qubit. Nat. Nanotechnol. 12, 16–20 (2017).
5. Teske, J. D. et al. A machine learning approach for automated fine-tuning of semiconductor spin qubits. Appl. Phys. Lett. 114, 133102 (2019).
Cited by
48 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献