image:
(a) Flow of training the estimator. Training data for the CNN was prepared by simulation using the CI model. The researchers simplified the data through preprocessing and trained the CNN. (b) Flow of estimating the charge state in experimental data. The researchers simplified the data through preprocessing and input the trained estimator to estimate the charge state.
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Credit: Yui Muto et al.
A group of researchers has successfully demonstrated automatic recognition of charge states in quantum dot devices using machine learning techniques, a significant step towards automating the preparation and tuning of quantum bits (qubits) for quantum information processing.
Semiconductor qubits use semiconductor materials to create the qubits. These materials are commonly used in traditional electronics and can therefore be integrated with traditional semiconductor technology. This compatibility has led scientists to consider semiconductor qubits as a promising candidate for future qubits to enable quantum computers.
In semiconductor spin qubits, the spin state of an electron confined in a quantum dot serves as the fundamental unit of data, or qubit. Forming these qubit states requires tuning a large number of parameters, including gate voltages, which is performed by human experts.
However, as the number of quantum bits increases, the number of parameters becomes too large and tuning becomes complicated, posing a problem in realizing large-scale computers.
“To overcome this, we developed a means to automate the estimation of the charge state in double quantum dots, which is essential for creating spin qubits, in which each quantum dot houses one electron,” says Tomohiro Otsuka, associate professor at the Advanced Institute for Materials Research at Tohoku University (WPI-AIMR).
Otsuka and his team used charge sensors to obtain a charge stability diagram and identify the optimal combination of gate voltages that ensures that there is exactly one electron per dot. To automate this tuning process, they needed to develop an estimator that could classify the charge state based on changes in the charge transition lines in the stability diagram.
To build this estimator, the researchers employed a convolutional neural network (CNN) trained on data prepared using a lightweight simulation model, the Constant Interaction model (CI model). Preprocessing techniques improved the data's simplicity and noise resistance, optimizing the CNN's ability to accurately classify charge states.
After testing the estimator on experimental data, initial results showed that it could effectively estimate most charge states, but that some states had high error rates. To address this, the researchers used Grad-CAM visualization to uncover decision-making patterns within the estimator. They found that errors were often caused by random noise that was misinterpreted as charge transition lines. By tuning the training data and refining the estimator structure, the researchers significantly improved accuracy for previously error-prone charge states, while maintaining high performance for other charge states.
“Using this estimator, we can automatically tune the parameters of semiconductor spin qubits, which is necessary for scaling up quantum computers,” adds Otsuka. “Furthermore, we have demonstrated that visualizing decision criteria that have previously been black boxes can serve as guidelines for improving the performance of the estimator.”
Details of the study were published in the journal. APL Machine Learning April 15, 2024.
About the World Premier International Research Center Initiative (WPI)
The World Premier International Research Center Initiative was launched by the Ministry of Education, Culture, Sports, Science and Technology in 2007 to foster world-class research environments and centers of excellence. These research centers, run by more than 10 research institutions across the country, are given a high degree of autonomy to pursue innovative operations and research. The program is run by the Japan Society for the Promotion of Science (JSPS).
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Advanced Institute for Materials Research (AIMR)
Tohoku University
Establishing a world-leading materials science research center
AIMR brings together outstanding researchers in the fields of physics, chemistry, materials science, engineering, and mathematics, and has created a world-class research environment. As a world-leading research center in materials science, the AIMR aims to push the boundaries of research and contribute to society.
journal
APL Machine Learning
Article Title
A visual explanation of a machine learning model for predicting the charge state of quantum dots
Article publication date
April 15, 2024
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