Skip to main content

​
Unified Execution Environment

Dynex presents all supported compute backends as standardized execution resources within a common runtime environment. These resources may include:
  • proprietary Dynex compute systems,
  • large-scale software-based emulation resources, and
  • third-party quantum processing units operated by external providers.
A centralized orchestration layer manages workload submission, routing, execution coordination, and result handling. From the user’s perspective, workloads are expressed once and executed consistently, independent of the underlying compute modality.

​
Hybrid and Heterogeneous Workflows

The platform is architected to enable hybrid computational workflows, allowing multiple computing paradigms to be combined seamlessly within the lifecycle of a single problem. Rather than binding a workload to a fixed execution model, the system supports flexible orchestration across heterogeneous compute substrates. As a result, different stages of a computation—such as preprocessing, probabilistic sampling, optimization, or refinement—can be executed on the hardware or simulation environment best suited to the specific task. Depending on availability, suitability, and performance requirements, workloads can be dynamically mapped to different backend resources, including classical high-performance systems, quantum emulation environments, neuromorphic probabilistic processors, or emerging room-temperature quantum hardware. Backend selection mechanisms evaluate factors such as the mathematical structure of the problem, the required computational precision, latency or throughput constraints, and the current availability of resources within the platform. Importantly, this orchestration layer is designed to remain transparent to the application developer. Developers interact with the platform through a unified programming interface and abstract problem formulations (e.g., QUBO, Ising, or probabilistic graphical models), while the platform handles the underlying execution strategy. This abstraction allows users to focus on modeling and algorithm design, while the system automatically determines the most effective execution pathway across the available heterogeneous computing infrastructure.