Scalable Autotuning of High-Temperature Quantum Dot Spin Qubits

As quantum dot (QD) spin qubits deploy in small arrays [1], achieving robust and scalable autotuning—particularly at elevated temperatures—remains a formidable challenge. A major hurdle arises from trapped charges within the oxide layers, which induce random offset voltage shifts on gate electrodes, with magnitudes reaching approximately 650 mV in state-of-the-art devices [1]. In this talk, we introduce a streamlined, five-step physically intuitive framework for initializing and bootstrapping QD devices [2]. We demonstrate this methodology experimentally at 1.3 K using an autotuner—BATIS (Bootstrapping Autonomously Testing Initialization System)—to configure a quad-QD Si/SiGe heterostructure device. We will also discuss our development of an open-source software platform designed to facilitate the deployment and scaling of DAG-based autotuning algorithms. This platform-agnostic approach addresses a critical bottleneck in quantum dot scalability, paving the way for the broader implementation of large QD arrays.

[1] S. Neyens, et. Al., Probing single electrons across 300-mm spin qubit wafers, Nature 629, 80 (2024).

[2] T. Kovach, et. Al., BATIS: Bootstrapping, Autonomous Testing, and Initialization System for Quantum Dot Devices, arxiv: 2412.07676 (2024).