Spin-qubit operation is framed as a modular automation problem with five modules to enable scalable stable arrays via interfaces, standardized data products, and workflow metrics.
Tuning Arrays with Rays: Physics- Informed Tuning of Quantum Dot Charge States
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 2representative citing papers
Neural-network autotuning combined with FPGA-accelerated RF reflectometry reduces stability-diagram acquisition time by 9.8x and total single-electron-regime initialization time by 2.2x in a SiGe quantum dot.
citing papers explorer
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Overcoming Configuration Bottleneck: Modular Pathways to Stable Semiconductor Spin-Qubit Arrays
Spin-qubit operation is framed as a modular automation problem with five modules to enable scalable stable arrays via interfaces, standardized data products, and workflow metrics.
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Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
Neural-network autotuning combined with FPGA-accelerated RF reflectometry reduces stability-diagram acquisition time by 9.8x and total single-electron-regime initialization time by 2.2x in a SiGe quantum dot.