Overcoming Configuration Bottleneck: Modular Pathways to Stable Semiconductor Spin-Qubit Arrays

Abstract

Over the past decade, semiconductor spin qubits have progressed from few-qubit demonstrations towards larger-scale devices fabricated in increasingly reproducible academic and industrial processes. This progress marks an inflection point: the central challenge is no longer to demonstrate high-fidelity operation in carefully tuned devices, but to discover, verify, and maintain stable operating conditions reliably across many interdependent controls, varied device geometries, and disparate material platforms. In this Perspective, we frame spin-qubit operation as a modular automation problem. We decompose the workflow into five modules: bootstrapping from minimal prior information, configuration tuning, virtualization of physical gates into effective control axes, qubit-level tuning, and an operation layer with drift-aware maintenance. Using recent demonstrations from our work and the broader community, we argue that scalability will depend on explicit interfaces between modules, standardized intermediate data products, and workflow-level metrics such as throughput, success probability, stability time, recovery time, and robustness. We close by outlining the infrastructure needed to move beyond isolated tuning demonstrations toward sustained operation: qubit-performance-aware feedback, reusable software and benchmark tasks, and tight collaboration among experimental, theoretical, and software efforts.