The management of observing proposals is a critical operational component for modern astronomical facilities. As missions grow in complexity, the demand for efficient, fair, and adaptable proposal handling systems is increasingly pressing. Existing systems are often monolithic and tightly coupled to a specific observatory, lacking the flexibility to be easily adapted. This paper introduces AstroPropose, a novel, general-purpose framework for creating and managing astronomical observing proposal systems, derived from the architecture of the operational Einstein Probe Observing Proposal System (EOPS). AstroPropose is centered on a powerful visual workflow engine, enabling administrators to define and deploy entire proposal workflows through a graphical interface. Key features include a dynamic form builder, a configurable workflow engine, and a flexible role-based access control (RBAC) system. We present the complete architecture and data model. As validation, we detail how the framework’s design principles are embodied in EOPS, which has successfully managed two annual proposal cycles and handles daily time-critical Target of Opportunity (ToO) submissions for the Einstein Probe mission. To further demonstrate the framework’s generalizability, we present a second case study: its adaptation as the proposal management prototype for the Chinese Space Station Telescope (CSST), a multi-instrument survey mission with a dual-phase review process. AstroPropose, validated through the successful operation of EOPS and the rapid prototyping of the CSST system, is being prepared for an open-source release to the astronomical community.

The large-scale structure (LSS) of the Universe, characterized by the distribution of clusters, filaments, walls, and voids, encodes fundamental information about cosmology and structure formation. Standard cosmological analyses, based on abundances as well as two-point and higher-order correlation functions, have been widely used to extract information from LSS. While powerful, these techniques can only access a subset of the full information content, leaving much of the encoded structure underexploited. This motivates the use of more flexible approaches capable of learning directly from the raw, graph-like nature of the cosmic web without relying on strong compression or dimensionality reduction. Recent advances in machine learning, particularly graph neural networks (GNNs), provide a natural framework for this task by capturing the non-Euclidean connectivity of cosmic structures. Building on these developments, recent progress in quantum machine learning (QML) offers an intriguing opportunity to further enhance representation power through quantum-enhanced models. In this work, we investigate the potential of quantum graph neural networks (QGNNs) as an alternative computational framework for analyzing LSS, focusing on structure identification and classification with an emphasis on distinguishing clusters and voids. Leveraging the hybrid quantum–classical paradigm implemented in PennyLane, we construct variational quantum circuits to encode graph representations of cosmological data and compare their performance against state-of-the-art classical GNNs. Our study evaluates the accuracy and expressivity of QGNNs relative to classical models, with particular attention to their ability to capture complex correlations in sparse, high-dimensional astrophysical datasets. Preliminary results indicate that QGNNs achieve comparable classification performance, while highlighting both the opportunities and current limitations of quantum-enhanced approaches in cosmological applications.

Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometre Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo relevant to some of the SKA science cases from HI intensity mapping, simulation of the radio surveys, radio source detection, the epoch of re-ionization and heliophysics. We discuss the capabilities, scalabilities and challenges of simulating large radio datasets in the context of SKA.

The ongoing exponential growth of computational power, and the growth of the commercial High Performance Computing (HPC) industry, has led to a point where ten commercial systems currently exceed the performance of the highest-used HPC system in radio astronomy in Australia, and one of these exceeds the expected requirements of the Square Kilometre Array (SKA) Science Data Processors. In order to explore implications of this emerging change in the HPC landscape for radio astronomy, we report results from a survey conducted via semi-structured interviews with 14 Australian scientists and providers with experience of commercial HPC in astronomy and similar data intensive fields. We supplement these data with learnings from two earlier studies in which we investigated the application of commercial HPC to radio astronomy data processing, using cases with very different data and processing considerations. We use the established qualitative research approach of thematic analysis to extract key messages from our interviews. We find that commercial HPC can provide major advantages in accessibility and availability, and may contribute to increasing researchers’ career productivity. Significant barriers exist, however, including the need for access to increased expertise in systems programming and parallelisation, and a need for recognition in research funding. We comment on potential solutions to these issues.