Optimization of Quantum-dot Qubit Fabrication via Machine Learning

Abstract

Precise nanofabrication represents a critical challenge to developing semiconductor quantum-dot qubits for practical quantum computation. Here, we design and train a convolutional neural network to interpret in-line scanning electron micrographs and quantify qualitative features affecting device functionality. The high-throughput strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space and by demonstrating a new approach to address lithographic proximity effects. The present results emphasize the benefits of machine learning for developing robust processes, shortening development cycles, and enforcing quality control during qubit fabrication.

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Cited by in corpus (7)

• Colloquium: Advances in automation of quantum dot devices control
• Toward Robust Autotuning of Noisy Quantum Dot Devices
• Neural networks for on-the-fly single-shot state classification
• Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits
• Visual explanations of machine learning model estimating charge states in quantum dots
• Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
• QDFlow: A Python package for physics simulations of quantum dot devices