Quantum device fine-tuning using unsupervised embedding learning Article Swipe

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Physics Encoder Embedding Set (abstract data type) Fine-tuning Voltage Quantum dot Autoencoder Quantum Optoelectronics Exploit Unsupervised learning Gate voltage Algorithm Computer hardware Transistor Artificial intelligence Quantum mechanics Computer science Deep learning Operating system Programming language Computer security

N. M. van Esbroeck , D.T. Lennon , H. Moon , Vu Nguyen , Florian Vigneau , Leon C. Camenzind , Liuqi Yu , Dominik M. Zumbühl , G. Andrew D. Briggs , Dino Sejdinović , Natalia Ares ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1088/1367-2630/abb64c · OA: W2999748565

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimize this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.