Simulating the dynamics of open quantum systems with spatial structure and external control is an important challenge in quantum information science. Classical numerical solvers for such systems require integrating coupled master and field equations, which is computationally demanding for simulation and optimization tasks and often precluding real-time use in network-scale simulations or feedback control. We introduce a regional attention-based neural architecture that learns the spatiotemporal dynamics of structured open quantum systems. The model incorporates translational invariance of physical laws as an inductive bias to achieve scalable complexity, and supports conditioning on time-dependent global control parameters. We demonstrate learning on two representative systems: a driven dissipative single qubit and an electromagnetically induced transparency (EIT) quantum memory. The model achieves high predictive fidelity under both in-distribution and out-of-distribution control protocols, and provides substantial acceleration up to three orders of magnitude over numerical solvers. These results demonstrate that the architecture establishes a general surrogate modeling framework for spatially structured open quantum dynamics, with immediate relevance to large-scale quantum network simulation, quantum repeater and protocol design, real-time experimental optimization, and scalable device modeling across diverse light-matter platforms.

This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning Control Fields (RCF)--a novel test-time approach that injects structured control signals to guide reasoning from a tree search perspective. RCF enables models to adjust reasoning effort according to given control conditions when solving complex tasks. Additionally, we present the Control-R-4K dataset, which consists of challenging problems annotated with detailed reasoning processes and corresponding control fields. To further enhance reasoning control, we propose a Conditional Distillation Finetuning (CDF) method, which trains model--particularly Control-R-32B--to effectively adjust reasoning effort during test time. Experimental results on benchmarks such as AIME2024 and MATH500 demonstrate that our approach achieves state-of-the-art performance at the 32B scale while enabling a controllable Long CoT reasoning process (L-CoT). Overall, this work introduces an effective paradigm for controllable test-time scaling reasoning.

Field-based reactive control provides a minimalist, decentralized route to guiding robots that lack onboard computation. Such schemes are well suited to resource-limited machines like microrobots, yet implementation artifacts, limited behaviors, and the frequent lack of formal guarantees blunt adoption. Here, we address these challenges with a new geometric approach called artificial spacetimes. We show that reactive robots navigating control fields obey the same dynamics as light rays in general relativity. This surprising connection allows us to adopt techniques from relativity and optics for constructing and analyzing control fields. When implemented, artificial spacetimes guide robots around structured environments, simultaneously avoiding boundaries and executing tasks like rallying or sorting, even when the field itself is static. We augment these capabilities with formal tools for analyzing what robots will do and provide experimental validation with silicon-based microrobots. Combined, this work provides a new framework for generating composed robot behaviors with minimal overhead.

We propose a scheme leveraging reinforcement learning to engineer control fields for generating non-classical states. It is exemplified by the application to prepare spin squeezed state for an open collective spin model where a linear control term is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from coherent spin state in an environment characterized by dissipation and dephasing. When compared to constant control scenarios, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performance of the outcomes. Furthermore, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. And thermal excitations of the reservoir are detrimental to the control outcomes. It should be confirmed that this is an open-loop strategy by closed-loop simulation, circumventing collapse of quantum state induced by measurements. Thanks to the flexible replaceability of the optimization modules and the controlled system, this research paves the way for its application in manipulating other quantum systems.