Adaptive Scheduler is an advanced job scheduling solution designed to efficiently manage and execute numerous adaptive learning tasks across large-scale computing clusters, ranging from 10,000 to over 100,000 cores. It seamlessly integrates with the Adaptive package, facilitating the parallel execution of `adaptive.Learner` instances using various backends such as `mpi4py.futures`, `ipyparallel`, `loky`, `concurrent.futures.ProcessPoolExecutor`, or `dask.distributed`. This scheduler is particularly adept at handling the challenges associated with high-core-count computations, ensuring optimal performance and resource utilization. Key Features and Functionality: - Scalability: Capable of efficiently managing computations on clusters exceeding 30,000 cores, addressing the limitations of traditional parallel computing tools that struggle with high core counts. - Adaptive Integration: Designed to work seamlessly with the Adaptive package, allowing for the execution of adaptive sampling algorithms that require real-time feedback and decision-making. - Fault Tolerance: Automatically handles job failures by rescheduling tasks, ensuring minimal data loss and continuous computation, even in the event of node crashes or evictions. - Minimal File System Load: Optimized to reduce the burden on the file system, enhancing overall system performance and reliability. - Automated Job Management: Eliminates the need for manual job script creation and submission by automating these processes, thereby reducing boilerplate code and potential errors. - Preservation of Computational State: Maintains Python kernel and variable states within jobs, allowing for consistent and uninterrupted computations without the need for reinitialization. - Computation Locality: Ensures that jobs continue to run independently, even if the central job manager fails, by maximizing computation locality and reducing inter-process communication overhead. Primary Value and Problem Solved: Adaptive Scheduler addresses the critical challenge of executing large-scale, adaptive computations that require real-time feedback and decision-making. Traditional parallel computing tools often falter under the demands of high-core-count environments due to centralized scheduling bottlenecks and communication overhead. By decentralizing job management and optimizing resource allocation, Adaptive Scheduler enables researchers and engineers to perform massive parallel computations efficiently. This capability is particularly valuable in fields such as quantum device simulations, where adaptive sampling algorithms necessitate dynamic and responsive computation strategies. By providing a robust, fault-tolerant, and scalable solution, Adaptive Scheduler empowers users to harness the full potential of modern supercomputing resources directly from a Jupyter notebook environment.