Quantum device fine-tuning using unsupervised embedding learning

Abstract

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 optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

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• Colloquium: Advances in automation of quantum dot devices control
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• Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
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• Miniaturizing neural networks for charge state autotuning in quantum dots
• Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays
• An automated approach for consecutive tuning of quantum dot arrays
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• Identifying Pauli spin blockade using deep learning
• Fully autonomous tuning of a spin qubit
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• Visual explanations of machine learning model estimating charge states in quantum dots
• Topological gap protocol based machine learning optimization of Majorana hybrid wires
• Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams
• Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
• Machine-learning-enabled characterization of individual ring resonators in integrated photonic lattices
• QDFlow: A Python package for physics simulations of quantum dot devices