Bootstrapping, autonomous testing, and initialization system for Si/Si$_x$Ge$_{1-x}$ multi-quantum-dot devices

TL;DR

This paper presents BATIS, an autonomous system for efficient calibration and testing of complex Si/SiGe quantum dot devices, reducing setup time and enabling scalable quantum computing hardware initialization.

Contribution

Introduction of BATIS, a physically intuitive, platform-agnostic autonomous framework for QD device evaluation and calibration that handles high-dimensional control spaces and trapped charge effects.

Findings

Successfully demonstrated at 1.3 K on a quad-QD device

Eliminates the need for deep cryogenic environments during initial diagnostics

Requires minimal prior knowledge of device architecture

Abstract

Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from trapped charges within the oxide layers, which induce random offset voltage shifts on gate electrodes, with a standard deviation of approximately 83 mV of variation within state-of-the-art present-day devices. Efficient characterization and tuning of large arrays of QD qubits depend on choices of automated protocols. Here, we introduce a physically intuitive framework for a bootstrapping, autonomous testing, and initialization system (BATIS) designed to streamline QD device evaluation and calibration. BATIS navigates high-dimensional gate voltage spaces, automating essential steps such as leakage testing, formation of all current channels, and gate characterization in the presence of trapped charges. For forming the current channels, BATIS follows a non-standard approach that requires a single set of measurements regardless of the number of channels. Demonstrated at 1.3 K on a quad-QD Si/Si$_x$Ge$_{1-x}$ device, BATIS eliminates the need for deep cryogenic environments during initial device diagnostics, significantly enhancing scalability and reducing setup times. By requiring only minimal prior knowledge of the device architecture, BATIS represents a platform-agnostic solution, adaptable to various QD systems, which bridges a critical gap in QD autotuning.