Solid-state color-center quantum sensors: materials, engineering strategies, and machine learning-enabled advances.

Hamdin Ozden, Birol Ozturk
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Abstract

Solid-state color-center spin defects provide a versatile platform for quantum sensing with applications ranging from nanoscale magnetometry to thermometry and electrometry. This review surveys recent progress in nitrogen-vacancy centers in diamond and defect spins in silicon carbide (SiC), with emphasis on their material properties, fabrication strategies, and coherence engineering. Key defect creation and optimization techniques, including ion implantation, femtosecond laser writing, focused ion beam nanofabrication, isotopic engineering, and surface passivation, are discussed in the context of improving spin coherence and optical performance. Fundamental quantum measurement protocols, optically detected magnetic resonance, Rabi oscillations, Ramsey interferometry, and spin echo, are reviewed as a hierarchical framework for characterizing spin control, dephasing, and intrinsic coherence. The review further highlights microscale sensing architectures and examines the growing role of machine learning in pulse optimization, adaptive estimation, and data-driven imaging. Finally, current challenges and future directions are outlined, focusing on scalability, environmental robustness, and autonomous operation. Together, these developments position diamond and SiC color centers as leading platforms for next-generation solid-state quantum sensors.

固态色心量子传感器:材料、工程策略和机器学习支持的进展。
固态色心自旋缺陷为量子传感提供了一个通用的平台,其应用范围从纳米级磁强计到测温和电测量。本文综述了金刚石中氮空位中心和碳化硅中缺陷自旋的研究进展,重点介绍了它们的材料特性、制备策略和相干工程。在提高自旋相干性和光学性能的背景下,讨论了关键缺陷的产生和优化技术,包括离子注入、飞秒激光写入、聚焦离子束纳米加工、同位素工程和表面钝化。基本的量子测量协议,光学检测磁共振,拉比振荡,拉姆齐干涉测量和自旋回波,作为表征自旋控制,消相和本征相干的层次框架进行了回顾。这篇综述进一步强调了微尺度传感架构,并探讨了机器学习在脉冲优化、自适应估计和数据驱动成像方面日益增长的作用。最后,概述了当前的挑战和未来的方向,重点是可伸缩性、环境健壮性和自主操作。总之,这些发展使金刚石和SiC色心成为下一代固态量子传感器的领先平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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