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Found 5 papers, 3 papers with code
Toward Robust Autotuning of Noisy Quantum Dot Devices
In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module.
Theoretical bounds on data requirements for the ray-based classification
The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases.
Ray-based framework for state identification in quantum dot devices
Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates.
Ray-based classification framework for high-dimensional data
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice.
Machine Learning techniques for state recognition and auto-tuning in quantum dots
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i. e. tuning up devices.
Quantum Physics
