研究目的
To automate the tuning of quantum-dot qubits into specific charge states using machine learning algorithms.
研究成果
The study successfully implements a machine-learning-assisted autotuner for setting the plunger gate voltages to reach any charge configuration of a DQD. The algorithm achieves a success rate of 57% in experimental tests, with the primary error source identified as a weak signal-to-noise ratio. Future improvements could involve more finely grained measurements and targeted sampling of the charge-stability diagram.
研究不足
The main limitation is the weak signal-to-noise ratio in some measurements, leading to false classifications. Additionally, the algorithm's success rate decreases with the complexity of the charge state transitions.
1：Experimental Design and Method Selection:
The study employs convolutional neural networks to recognize transitions between charge states in the charge-stability diagram. The algorithm is designed to tune a double quantum dot (DQD) from an unknown charge state to a predefined charge state.
2：Sample Selection and Data Sources:
The algorithm is trained and tested on a GaAs double-quantum-dot device. Data for training the neural networks are obtained from experimentally measured charge-stability diagrams.
3：List of Experimental Equipment and Materials:
The device consists of a GaAs-(Al,Ga)As heterostructure with gold gates to confine electrons, capable of forming up to three quantum dots (QDs).
4：Experimental Procedures and Operational Workflow:
The tuning process is split into two stages: finding a reference point where the DQD is completely empty and then loading electrons to reach the desired charge state. Each stage uses a different neural network model to recognize charge transitions.
5：Data Analysis Methods:
The performance of the algorithm is evaluated based on its accuracy in identifying charge transitions and its success rate in reaching the desired charge state.
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