AI-powered micro-comb lasers: a new approach to transfer portable atomic clock accuracy in integrated photonics

光频梳是一种激光，其频谱由一系列与精确的光脉冲序列相对应的谱线组成。当在便携式和节能平台上小型化时，这些激光器可以提供革命性量子技术所需的精确跳动的“光学心脏”，例如便携式光学原子钟，重力传感器和双梳光谱仪。正如NQTP战略计划中概述的那样，这些技术可以通过彻底改变医疗保健、移动性、金融交易和下一代移动基础设施来改变我们的社会。在过去的15年里，小型化频率梳的科学已经达到了令人印象深刻的技术成熟水平。面向紧凑和节能设备的既定平台是微梳，这是一类基于小型化非线性谐振器的激光器。尽管现在广泛的努力已经使微型化触手可及，但微型梳子在高能耗应用（如便携式原子钟和宽带电信）所要求的高功率排放条件下仍然难以控制，这令人惊讶。在所有这些领域中，微梳频谱应该尽可能宽（即，八度跨度），谱线具有特定的特征（例如，电信频带的平坦度），同时携带足够的光能来锁定外部参考。在微梳平台中，满足这些要求仍然是特别具有挑战性的，其中超快脉冲源于数千个光波的相互作用和同步。当接近大功率排放时，这些相互作用变得越来越难以控制，导致混乱和不可预测的排放。因此，最先进的微型梳子被限制在相对较低的光学功率，其中非线性相互作用更容易被控制，标准的稳定技术仍然适用。令人惊讶的是，这种限制在许多激光技术中普遍存在，其中高能发射状态基本上仍然是未知的领域。在这方面，高度非线性激光是复杂系统如大脑、天气和社会的光子对应物。在这些系统中，大量相互作用的元素和高度的非线性为产生高级功能提供了必要的成分。然而，复杂性也使我们无法定义通用的、可解释的模型来理解和控制这些系统的演化。学习如何驯服极端丰富的复杂交互需要在概念和方法上进行深刻的范式转变。今天，由于人工智能（AI）技术的惊人进步，这种转变离我们更近了一步。该项目的愿景是通过开发一种新型的人工智能驱动激光器，在微型梳子领域实现这种范式转变，这种激光器能够“学习”如何在实时和现实实验条件下优化其发射。人工智能正在成为在标准发射条件下稳定超快激光器的理想工具，在很短的时间内提供更好的性能。然而，驱动和维持微梳激光器进入任意的、传统上不稳定的发射状态需要扩展人工智能预测，使用复杂的系统内部动力学物理模型，而不一定是先验的。为了填补这一空白，我将建立并领导一个跨学科的研究小组，开发一种基于数据驱动发现的新方法，这是一个新兴的理论框架，将人工智能强大的数据处理能力与动力系统和非线性控制理论的概念相结合。这种方法将允许识别驱动现实生活中的微梳系统的“隐藏”非线性效应，为应用先进的控制策略和设计全新一代的微梳开辟一条独特的途径，这是现有方法无法想象的

Optical frequency combs are lasers with a frequency spectrum composed of a sequence of lines corresponding to precise trains of optical pulses in time. When miniaturised in portable and energy-efficient platforms, these lasers can provide a precisely beating "optical heart" required by transformative quantum technologies, such as portable optical atomic clocks, gravitational sensors, and dual-comb spectrometers. As outlined in the NQTP strategic plan, these technologies can transform our society by revolutionising healthcare, mobility, financial transactions, and next-generation mobile infrastructures.In the last fifteen years, the science of miniaturised frequency combs has reached an impressive level of technological maturity. The established platform towards compact and energy-efficient devices is micro-combs, a class of lasers based on miniaturised nonlinear resonators. While extensive efforts have now brought miniaturisation within grasp, micro-combs remain surprisingly hard to control at the high-power emissions regimes required by energy-demanding applications, such as portable atomic clocks and broadband telecommunications. In all these domains, the micro-comb spectrum should be as broad as possible (i.e., octave-spanning), with spectrum lines possessing specific features (e.g., flatness across telecom bands) while carrying enough optical energy to lock to external references. Meeting these requirements remains particularly challenging in micro-comb platforms, where the ultrafast pulses originate from the interaction and synchronisation of thousands of optical waves. These interactions become increasingly hard to control when approaching high power emissions, leading to chaotic and unpredictable emissions. As a result, state-of-the-art micro-combs are restricted to relatively low optical powers, where nonlinear interactions are easier to tame, and standard stabilisation techniques still apply. This limitation is surprisingly ubiquitous across many laser technologies, where high-energy emission states have remained substantially uncharted territory. In this regard, highly nonlinear lasers are the photonic counterpart of complex systems like the brain, weather and society. In these systems, a large number of interacting elements and a high degree of nonlinearity provide an essential ingredient to produce high-level functionalities. However, complexity also eludes the definition of universal, interpretable models to understand and control the evolution of these systems. Learning how to tame the extreme richness of complex interactions requires a profound paradigm shift in concepts and methodology. This transformation is today one step closer thanks to the impressive advances in Artificial Intelligence (AI) technologies. This project's vision is to bring such a paradigm shift in the field of micro-combs by developing a new class of AI-powered lasers capable of "learning" how to optimise their emission in real-time and in real-life experimental conditions. AI is emerging as an ideal tool to stabilise ultrafast lasers in standard emission regimes, delivering improved performance in a fraction of the time. Nevertheless, driving and maintaining a micro-comb laser into an arbitrary, traditionally unstable emission state requires extending AI predictions with sophisticated physical modelling of the system's internal dynamics not necessarily known a priori. To fill this gap, I will establish and lead an interdisciplinary group of researchers to develop a new methodology based on data-driven discovery, an emerging theoretical framework combining the powerful data-processing capabilities of AI with concepts from dynamical systems and nonlinear control theory. This approach will allow identifying the "hidden" nonlinear effects driving a real-life micro-comb system, opening a unique pathway to apply advanced control strategies and design entirely new generations of micro-combs, inconceivable with existing approach