# Real-Life Experimental Applications

In this video, we review contemporary experimental applications of machine learning charge tuning.

# Main takeaways

- Charge tuning is nowadays routinely embedded into quantum dot experiments in many labs around the world.
- Mode-advanced modifications of the method introduced here have been proposed and experimentally verified.

# Further thinking

What is the alternative to measuring charge diagram patches for charge tuning?

a. Measuring quantum volume

b. Measuring rays

c. Measuring multiple quantum dots

d. Measuring multiple patches at once

# 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.