用Transformer直接分析量子点电荷图，提升校准效率与通用性。
End-to-End Analysis of Charge Stability Diagrams with Transformers
- 采用目标检测Transformer直接解析电荷稳定性图
- 在三种量子比特架构上均优于传统CNN，且无需重新训练
- 适合需要快速、通用校准的量子计算设备开发者
Transformer模型与端到端学习框架正迅速革新人工智能领域。本文将目标检测Transformer应用于半导体量子点阵列的电荷稳定性图分析，这是实现基于自旋的量子计算可扩展性的关键任务。我们的模型能够识别三重点及其连接关系，对虚拟栅极校准、电荷态初始化、漂移修正和脉冲序列设计至关重要。结果显示，在三种不同自旋量子比特架构上，该模型性能均优于卷积神经网络，且无需重新训练。相较于现有方法，本方法显著降低复杂度与运行时间，同时提升泛化能力。结果表明，基于Transformer的端到端学习框架有望成为可扩展、设备与架构无关的量子点器件控制与调谐工具的基础。
原文摘要 · Abstract (English)
Transformer models and end-to-end learning frameworks are rapidly revolutionizing the field of artificial intelligence. In this work, we apply object detection transformers to analyze charge stability diagrams in semiconductor quantum dot arrays, a key task for achieving scalability with spin-based quantum computing. Specifically, our model identifies triple points and their connectivity, which is crucial for virtual gate calibration, charge state initialization, drift correction, and pulse sequencing. We show that it surpasses convolutional neural networks in performance on three different spin qubit architectures, all without the need for retraining. In contrast to existing approaches, our method significantly reduces complexity and runtime, while enhancing generalizability. The results highlight the potential of transformer-based end-to-end learning frameworks as a foundation for a scalable, device- and architecture-agnostic tool for control and tuning of quantum dot devices.
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