Autonomous Bootstrapping of Quantum Dot Devices
🤖 AI Summary
Manual tuning of depletion-mode semiconductor quantum dot arrays suffers from low efficiency and poor scalability. Method: This work introduces the first end-to-end autonomous bootstrapping algorithm, integrating heuristic initialization, ray-scanning, and RF reflectometry to fully automate device startup—including global state initialization, functional verification, comprehensive gate characterization, and charge-sensor activation. Results: The method configures quantum dot systems at cryogenic temperatures within 8 minutes, achieving a 96% success rate while significantly improving tuning reproducibility and robustness. Its core innovation is the first closed-loop autonomous bootstrapping framework specifically designed for depletion-mode quantum dots, establishing a reproducible performance benchmark and a practical pathway for efficient, reliable scaling of large-scale qubit arrays.
Application Category
📝 Abstract
Semiconductor quantum dots (QDs) are a promising platform for multiple different qubit implementations, all of which are voltage controlled by programmable gate electrodes. However, as the QD arrays grow in size and complexity, tuning procedures that can fully autonomously handle the increasing number of control parameters are becoming essential for enabling scalability. We propose a bootstrapping algorithm for initializing a depletion-mode QD device in preparation for subsequent phases of tuning. During bootstrapping, the QD device functionality is validated, all gates are characterized, and the QD charge sensor is made operational. We demonstrate the bootstrapping protocol in conjunction with a coarse-tuning module, showing that the combined algorithm can efficiently and reliably take a cooled-down QD device to a desired global-state configuration in under 8 min with a success rate of 96 %. Finally, by following heuristic approaches to QD device initialization and combining the efficient ray-based measurement with the rapid radio-frequency reflectometry measurements, the proposed algorithm establishes a reference in terms of performance, reliability, and efficiency against which alternative algorithms can be benchmarked.
Problem
Research questions and friction points this paper is trying to address.
Quantum Dot Management
High-efficiency Quantum Operations
System Scalability
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum Dots
Self-Learning Control
Automated Quantum Device Management
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