Abstract: A system is disclosed for generating a common intelligence picture through latent space fusion and threat forecasting. Priority intelligence requirements are processed using language and machine learning models to generate structured tasking and prioritize multi-modal sensor collection. Data from geospatial, cyber, radio frequency, and behavioral sources is embedded into domain-specific representations and fused into a unified latent space. The system models behavioral trajectories, detects anomalies, and maintains digital twins of entities and regions. Deviations from expected behavior may trigger alerts, causal inference, attribution hypotheses, and deterrence simulations. Adversary behavior is forecast in latent space, enabling evaluation of intervention strategies with impact and timing assessments. Visualizations, alerts, and reports are generated based on latent divergence and simulation outputs.

Abstract: Examples of the present disclosure provide systems and methods for performing quantum simulation. For example, such systems and methods may perform quantum simulation, at least in part, by obtaining a Hamiltonian equation and a selection of a target quantum device, accessing an abstract analog instruction set configured to cause an evolution in the selected target quantum device, and compiling the Hamiltonian equation to generate a pulse schedule based on the abstract analog instruction set for the target quantum device.