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Found 6 papers, 3 papers with code
Bridging the reality gap in quantum devices with physics-aware machine learning
The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices.
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability.
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning.
Quantum device fine-tuning using unsupervised embedding learning
Quantum devices with a large number of gate electrodes allow for precise control of device parameters.
Machine learning enables completely automatic tuning of a quantum device faster than human experts
Device variability is a bottleneck for the scalability of semiconductor quantum devices.
Efficiently measuring a quantum device using machine learning
Scalable quantum technologies will present challenges for characterizing and tuning quantum devices.
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