Matter at the atomic-scale is inherently governed by the laws of quantum mechanics. This makes charges and spins confined to individual atoms—and interactions among them—an invaluable resource for fundamental research and quantum technologies alike. However, harnessing the inherent ‘quantumness’ of atomic-scale objects requires that they can be precisely engineered and addressed at the individual atomic level. Since its invention in the 1980s, scanning tunnelling microscopy (STM) has repeatedly demonstrated the unrivalled ability to not only resolve but manipulate matter at atomic length scales. Over the past decades, this has enabled the design and investigation of bottom-up tailored nanostructures as reliable and reproducible platforms to study designer quantum physics and chemistry, band topology, and collective phenomena. The vast range of STM-based techniques and modes of operation, as well as their combination with electromagnetic fields from the infrared to microwave spectral range, has even allowed for the precise control of individual charge and spin degrees of freedom. This roadmap reviews the most recent developments in the field of atomically-engineered quantum platforms and explores their potential in future fundamental research and quantum technologies.

This paper addresses the challenges of deploying reinforcement learning (RL) models for traffic signal control (TSC) in real-world environments. Real-world training can prevent mismatches between simulation environments and the actual traffic conditions, thereby achieving better performance of agent upon deployment. However, free explorations by agents during real-world training can disrupt traffic operations. To mitigate this, this paper proposes a reference mechanism to guide the decision-making process within the RL framework. A reference timing policy, typically a model-based signal strategy, is integrated into the learning process to provide agents with reference actions. Specifically, an additional Q-value function is introduced to evaluate both the agent's actions and those of the reference policy, allowing for adjustments before the actions are executed in real traffic system. Numerical results indicate that the reference mechanism effectively enhances system performance early in the training process, thus accelerating learning. We also combine the reference RL method with a pretraining procedure and a jump-start algorithm, respectively. Experimental results demonstrate their effectiveness in further enhancing system performance and facilitating real-world training.

Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and automated vehicles (AVs), research has recently focused on studying the traffic flow characteristics of Adaptive Cruise Controls (ACCs), the most typical AV. However, in most works, the ACC system is studied under a simplifying and unrealistic assumption, or the ACC system modeled is inaccurate. This paper proposes a general hierarchical control system to model ACC systems with several assumptions based on the deficiencies above. Moreover, a field experiment was conducted, and the corresponding experimental data was used to verify the proposed hierarchical control system and assumptions. In addition, string stability is explored along with sensitivity analyses of control parameters based on an example under the constant time gap policy. The results show that different upper-level controller parameters have different delays, where the delay of the speed is negligible; the introduction of actuator delay and lag in the lower-level controller can significantly improve the model goodness of fit. Furthermore, optimizing the delay and lag in the lower-level controller can significantly enhance the string stability of ACCs than optimizing the control parameters.