# Supervised Learning with Feedback

In this video, we explain the concept of charge state tuning, how to envision it as a charge stability diagram data parsing problem and formulate charge tuning as a question machine learning algorithm.

# Main takeaways

- Charge states can be identified visually from the charge stability diagram.
- Automation of charge tuning can be visualised as shifts through the charge stability diagram.

# Further thinking

True or False: Charge tuning can be formulated as a counting of charge regions in a charge stability diagram.

# Further reading

Automated tuning of double quantum dots into specific charge states using neural networks: https://arxiv.org/abs/1912.02777

An example of automated tuning of a quantum dots device – from an unknown charge state into a pre-defined charge state, using a neural network.

Computer-automated tuning of semiconductor double quantum dots into the single-electron regime: https://arxiv.org/abs/1603.02274

Another example of automated tuning of the quantum dots device – from double quantum to single electron regime.

Automated tuning of inter-dot tunnel couplings in quantum dot arrays: https://arxiv.org/abs/1803.10352

Example of auto-tuning in a quantum dot device for the tunnel couplings via tuning the barrier gate voltages.

A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits: https://arxiv.org/abs/1901.01972

On the auto-tuning of gate voltages in quantum dot devices using machine learning algorithms.

Efficiently measuring a quantum device using machine learning: https://arxiv.org/abs/1810.10042

On the automated tuning of the measurements on quantum dot devices using machine learning algorithms.

Machine Learning techniques for state recognition and auto-tuning in quantum dots: https://arxiv.org/abs/1712.04914

On the auto-tuning of states and charge configurations of single and double quantum dot arrays using deep and convolutional neural networks.