此次新型分子量子比特的核心成分是稀土元素铒。由于其独特的物理特性，铒能够在保持光学跃迁"干 净"的同时，与磁场发生强烈相互作用，因此在经典光电子技术和新兴量子系统中均具有重要价值。新 设计的分子结构使得信息可被编码在其磁性自旋态中，并通过特定波长的光进行读取和操控——而这些 光的频率恰好与现有的硅基光子电路和光纤通信系统兼容。 来自美国芝加哥大学、加州大学伯克利分校、阿贡国家实验室以及劳伦斯伯克利国家实验室的科学家们 开发出一种新型分子量子比特，能够弥合光与磁之间的鸿沟，在与现有电信技术相同频率下运行。这项 突破性进展发表在新一期《科学》杂志上，为构建可扩展的量子技术提供了一种极具前景的新平台，且 有望与当前广泛使用的光纤网络实现无缝集成。 在量子技术中，光通常用于传输和测量量子态，而磁性相关的自旋则是量子计算、传感和存储的关键资 源。该研究巧妙结合了量子光学与合成化学两个领域：前者推动了激光与量子网络的发展，后者则在诸 如磁共振成像造影剂等应用中展现出强大能力，从而构建出能连接这两个领域的分子级功能单元。 研究同时证明，通过合成化学手段，可在分子尺度上精确设计和调控量子材料的行为。这为进一步开发 面向量子网 ...

Core Insights - The integration of artificial intelligence (AI) and automation in synthetic chemistry is seen as the future, enhancing efficiency and reducing reliance on traditional trial-and-error methods [1][3][4] - The vastness of chemical space presents significant challenges for chemists, with the theoretical number of small molecules that can be synthesized reaching 10^60, far exceeding the number of stars in the universe [2][3] - Current methodologies in synthetic chemistry include "top-down" experimental approaches and "bottom-up" theoretical approaches, both facing efficiency and universality challenges, necessitating new tools [3][4] Group 1: Challenges in Synthetic Chemistry - Synthetic chemistry is fundamental for creating materials essential for agriculture, health, and industry, but faces increasing demands for new materials and performance [1][2] - The "top-down" approach relies on chemists' intuition and experience, while the "bottom-up" approach uses computational methods, both of which have limitations in efficiency and applicability [2][3] Group 2: Automation and AI in Research - Automation in laboratories, such as high-throughput technology, has been adopted to enhance efficiency in catalyst development, significantly reducing the time required for experiments [4][5] - The use of automated platforms allows researchers to design and test thousands of catalyst formulations quickly, leading to the discovery of new materials that would take much longer through traditional methods [5][6] Group 3: Future Directions - AI's role in chemistry is currently as a supportive tool rather than a replacement for human intuition, with significant potential for development in interpreting experimental results [6][8] - The concept of "self-driving laboratories" is emerging, where automated systems can analyze results and autonomously design subsequent experiments, creating a rapid iterative cycle of design, execution, and learning [9][10]