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arXiv:2606.30883

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

Justyna P. Zwolak, Anthony Sigillito

quant-phcond-mat.mes-hallphysics.app-phphysics.data-anSubmitted Jun 29, 2026• Announced Jul 1, 2026• 0 citations
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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.

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10 pages, 2 figures, 1 table

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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.
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Quantum computing has progressed from early demonstrations across superconducting, trapped ion, photonic, and spin-based platforms to increasingly integrated systems. However, transitioning from the noisy intermediate-scale quantum (NISQ) era to robust, fault tolerant implementations remain a central challenge. This paper presents a systematic review and synthesis of practical quantum computing, encompassing experimental advances, integrated photonics, error correction breakthroughs, software stack evolution, benchmarking practices, algorithmic readiness, and socio-technical dimensions. We critically assess current limitations and highlight emerging directions, including integrated photonic modules with high fidelity interconnects, real time decoders for surface code logical qubits, and hardware aware compiler strategies. Our analysis is grounded in peer-reviewed experimental data and model-based evaluations. Key findings include demonstrated logical error suppression via increased code distance, scalable photonic module prototypes, and refined resource estimates for cryptanalytic and simulation tasks. This paper concludes with a structured research agenda for 2025–2030, proposing four priority thrusts such as hardware QEC co design, reproducible cross platform benchmarking, modular/distributed architectures, and quantum networking with governance frameworks. This evidence based roadmap aims to align researchers and practitioners toward actionable milestones on the path to practical quantum computing.
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