EAGER: Quantum Manufacturing: Machine learning-powered deterministic nanoassembly of ultrafast quantum photonic devices

单光子忠实而快速地携带量子信息，使量子光子网络成为所有量子信息技术的关键组成部分。实现量子光子系统目前不可能在单一材料平台上实现，并且设备必须由不同的材料组装而成。量子光可以由纳米级粒子中的类原子系统产生，这可以为已建立的经典光子平台提供所需的量子功能。纳米粒子还与单光子发射的纳米光子增强兼容，有助于提高前端量子比特率，这是量子光子网络的一个长期问题。然而，对于用纳米颗粒实现的量子器件，没有既定的制造工艺。该团队将通过使用神经网络辅助控制来解决纳米颗粒后处理，选择和操作中的关键瓶颈，为这样的过程绘制蓝图。这项研究将带来独特的劳动力发展机会，包括在本科阶段的众包计划，以训练控制制造过程的神经网络。量子光子网络有望为未来的分布式量子计算机，安全通信链路和分布式量子传感器提供动力。目前，单一材料平台无法满足量子网络对量子光子器件的多样性和严格要求，因此迫切需要混合制造方法。基于纳米颗粒的量子发射体，例如纳米金刚石中的色心，与所有光子材料平台兼容。此外，纳米颗粒允许量子发射器与纳米级等离子体模式接口，在自发发射中提供多阶加速，并增加前端量子比特率。然而，小尺寸和异质性阻碍了纳米粒子在量子器件制造中的使用。由神经网络驱动的监督胶体合成，大规模筛选和确定性操纵的组合可以产生可扩展的量子器件制造过程。该团队将研究原位监测合成低损耗等离子体壳纳米金刚石核心结构，以增强单光子发射，开发快速全光学色心选择，以及神经网络驱动的基于原子力显微镜的拾取和放置程序。认识到机器学习在纳米技术中的预期即将产生的影响，该团队将开展旨在实现本科生参与和培训的教育活动。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Single photons carry quantum information faithfully and quickly, making quantum photonic networks a key component of all quantum information technologies. Realizing quantum photonic systems is not currently possible on a single material platform, and devices must be assembled from dissimilar materials. Quantum light can be produced by atom-like systems in nanoscale particles, which can supply the required quantum functionality to the established classical photonic platforms. Nanoparticles are also compatible with the nanophotonic enhancement of single-photon emission, helping to boost front-end quantum bitrates, a long-standing problem for quantum photonic networks. However, there is no established manufacturing process for quantum devices realized with nanoparticles. The team will produce a blueprint for such a process by addressing key bottlenecks in nanoparticle post-processing, selection, and manipulation using neural network-assisted control. This research will enable unique workforce development opportunities, including a crowdsourcing initiative at the undergraduate level to train neural networks controlling the manufacturing process.Quantum photonic networks promise to power future distributed quantum computers, secure communication links and distributed quantum sensors. The diverse and stringent requirements on quantum photonic devices for quantum networks cannot be currently satisfied on a single material platform, and a hybrid manufacturing approach is strongly desirable. Nanoparticle-based quantum emitters, such as color centers in nanodiamonds are compatible with all photonic material platforms. Additionally, nanoparticles allow the quantum emitters to be interfaced with nanoscale plasmonic modes, offering a multi-order speedup in spontaneous emission, and increasing the front-end quantum bitrates. However, the small size and heterogeneity hinder the use of nanoparticles in the manufacturing of quantum devices. A combination of supervised colloidal synthesis, large-scale screening, and deterministic manipulation, driven by neural networks, can yield a scalable quantum device manufacturing process. The team will investigate an in-situ monitored synthesis of low-loss plasmonic shell nanodiamond core structures for enhanced single-photon emission, develop rapid all-optical selection of color centers, and a neural-network driven atomic force microscope-based pick-and-place procedure. Recognizing the expected imminent impact of machine learning in nanotechnology, the team will pursue an educational activity aimed at achieving the participation and training of undergraduate students. A portion of the proposed process will be made available to the undergraduate community for crowdsourced online data generation and neural network training.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.