Abstract: Traditional approaches for recommending optimum combination of quantum circuits are experimentation based approaches, and require manual efforts or are cumbersome, effort intensive and iterative processes. Method and system disclosed herein generally relates to quantum experimentation, and, more particularly, for recommending optimum combination of quantum circuits. In this approach, a high-level combination of experiments are initially generated, which are further prioritized using a graph based approach, which then forms a training data. The training data is then used for generating a GNN data model, which is further used for recommending optimum combination of quantum circuits.

Abstract: The disclosure relates generally to methods and systems for optimizing multi-robot task allocation through heuristic-guided reinforcement learning in dynamic environments. Conventional RL framework-based approaches focus on optimal task selection, neglecting task-to-robot assignment under the assumption of constant robot availability post-selection. The present disclosure solves the technical problems in the art through heuristic-guided reinforcement learning (RL) in dynamic environments. The methods and systems of the present disclosure (coined as HeuRAL-MATE) combine heuristic guidance with RL to address the multi-robot task allocation challenge in warehouse environments. The HeuRAL-MATE effectively manages real-time task selection, the allocation of tasks to robots, and the secure navigation of robots, by minimizing both the total travel distance of robots and the delay in task execution while considering practical charging/discharging constraints and collision-free navigation of the robots.

Abstract: The challenge in managing power grid networks lies not only in dealing with the uncertainty of power demand and generation, or the uncertain events, but also with the huge action space even in a moderately-sized grid. In most such scenarios, the grid operator relies on his/her own experience or at best, some of the potential heuristics whose scope is limited to mitigating only a certain type of uncertainties. The present disclosure provides a heuristic-guided RL framework, for robust control of power networks subjected to production and demand uncertainty, as well as adversarial attacks. Using a careful action selection process, in combination with line reconnection and recovery heuristics, equips the present disclosure to outperform conventional approaches on several challenge datasets even with reduced action space. The present disclosure not only diversifies its actions across substations, but also learns to identify important action sequences to protect the network against targeted adversarial attacks.

Abstract: A data processing system implements a hybrid environment for interactions between remote and in-person users. The data processing techniques provide tools for facilitating mingling of remote and in-person users in semi-structured interaction, such as but not limited to tradeshows or conferences, and unstructured interactions, such as but not limited to social gatherings that solve the technical problems associated with enabling such systems. The data processing system implements audio porosity and map-based navigation to facilitate improved spatial awareness and awareness of the presence of other remote or in-person users nearby with whom the user can interact.

Abstract: The challenge in managing power grid networks lies not only in dealing with the uncertainty of power demand and generation, or the uncertain events, but also with the huge action space even in a moderately-sized grid. In most such scenarios, the grid operator relies on his/her own experience or at best, some of the potential heuristics whose scope is limited to mitigating only a certain type of uncertainties. The present disclosure provides a heuristic-guided RL framework, for robust control of power networks subjected to production and demand uncertainty, as well as adversarial attacks. Using a careful action selection process, in combination with line reconnection and recovery heuristics, equips the present disclosure to outperform conventional approaches on several challenge datasets even with reduced action space. The present disclosure not only diversifies its actions across substations, but also learns to identify important action sequences to protect the network against targeted adversarial attacks.

Abstract: Traditional approaches for recommending optimum combination of quantum circuits are experimentation based approaches, and require manual efforts or are cumbersome, effort intensive and iterative processes. Method and system disclosed herein generally relates to quantum experimentation, and, more particularly, for recommending optimum combination of quantum circuits. In this approach, a high-level combination of experiments are initially generated, which are further prioritized using a graph based approach, which then forms a training data. The training data is then used for generating a GNN data model, which is further used for recommending optimum combination of quantum circuits.