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Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

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Published on: June 3, 2015

Machine Learning-Assisted Precision Manufacturing of Atom Qubits in Silicon.

Aaron D Tranter¹, Ludwik Kranz^2,3, Sam Sutherland^2,3

¹Centre of Excellence for Quantum Computation and Communication Technology, Department of Quantum Science and Technology, Research School of Physics, The Australian National University, Acton 2601, Australia.

|July 17, 2024

Summary

This summary is machine-generated.

Machine learning accurately predicts donor atom numbers in silicon qubits during manufacturing. This breakthrough in scanning tunneling microscope (STM) lithography advances the development of scalable quantum computing and sensing technologies.

Keywords:

STM lithography machine learning phosphorus quantum dots silicon

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Area of Science:

Quantum computing
Materials science
Machine learning

Background:

Silicon donor-based qubits offer a scalable platform for quantum computing.
Precise donor atom placement is critical for qubit performance.
Current manufacturing methods lack real-time feedback on donor atom configuration.

Purpose of the Study:

To develop machine learning (ML) techniques for real-time prediction of donor atom numbers during qubit fabrication.
To enable automated manufacturing of high-fidelity quantum bits (qubits).

Main Methods:

Utilized machine learning image recognition on scanning tunneling microscope (STM) images.
Developed convolutional neural networks (CNNs) to predict donor atom distribution.
Implemented techniques to mitigate overfitting, including reduced model complexity, data preprocessing, and augmentation.

Main Results:

Achieved over 90% accuracy in predicting the donor atom number at the qubit site.
Demonstrated consistent performance across developed ML models.
Validated the effectiveness of ML in real-time manufacturing feedback.

Conclusions:

Machine learning provides an accurate and automated method for controlling donor atom configurations in silicon qubits.
This work is a significant step towards the automated fabrication of qubits for quantum computation and sensing.
The developed ML techniques can accelerate the scaling of quantum technologies.