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Dynex-operated compute systems are optimized for probabilistic and energy-based problem formulations commonly used in optimization, sampling, and inference. These systems are integrated into the Dynex platform as native execution resources and are accessed through the same APIs and tooling as other supported backends. From a platform architecture perspective, these systems can be understood as specialized accelerators for probabilistic and combinatorial computation, designed to complement rather than replace existing classical and quantum hardware infrastructures. Instead of operating as isolated computing paradigms, they form part of a heterogeneous compute stack in which different physical substrates are leveraged according to their strengths in solving optimization, sampling, and stochastic inference problems. Within this framework, the available computational resources span several technological layers. At one end, high-performance quantum emulation systems implemented on CPU/GPU nodes enable the simulation of large-scale quantum circuits and quantum-inspired algorithms with substantial scalability. In parallel, quantum-driven neuromorphic computing systems based on analog CMOS technology (Apollo Series) provide energy-efficient hardware substrates for probabilistic computing, enabling massively parallel exploration of energy landscapes typical of Ising and QUBO formulations. At the hardware frontier, a proprietary room-temperature quantum computing system built on nitrogen-vacancy (NV) diamond spin qubits (Zeus series) introduces a genuine quantum spin system that operates without cryogenic infrastructure, opening a pathway toward hybrid architectures where classical, neuromorphic, and quantum spin-based devices coexist within a unified computational ecosystem.