# Charge Tuning as a NN with Feedback

In this video, we map the charge tuning problem on a neural network with feedback. We show how to classify small patches of charge stability diagram and embed this classification within a Python feedback loop that allows us to navigate the charge states.

# Main takeaways

- Classify the type of charge transition, not a charge state.
- Use the output of neural network as an input for shifting function that counts the charge transition.

# Further thinking

What are the stages of the charge tuning we learned about?

a. Tuning to (0,0) followed by tuning to (m,n)

b. Tuning to (m-1,n-1) followed by tuning to (m-2, n-2)

c. Tuning charge state (m) followed by tuning charge state (n)

d. Tuning charge state (n) followed by tuning charge state (m)

# 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.