We present the UHF SZTM BorgAI OmniPoint Recursive Human–AI Co-Evolution framework for hybrid quantum–classical simulations on commercial hardware. Our work demonstrates how a CPU-first workflow can validate the robustness, stability, and reproducibility of quantum simulations before GPU acceleration enables real-time computation. All outputs—including eigenvalue and eigenvector overlaps, tunneling modifications, energy shifts, and stability indices—are fully logged and reproducible. This approach highlights the critical role of human-guided AI recursion in optimizing multi-domain scientific workflows and establishing a scalable, extensible platform for hybrid quantum–classical research. All results are testable, falsifiable, and publicly verifiable.
We present the UHF SZTM BorgAI OmniPoint Recursive Human–AI Co-Evolution framework for hybrid quantum–classical simulations on commercial hardware. Our work demonstrates how a CPU-first workflow can validate the robustness, stability, and reproducibility of quantum simulations before GPU acceleration enables real-time computation. All outputs—including eigenvalue and eigenvector overlaps, tunneling modifications, energy shifts, and stability indices—are fully logged and reproducible. This approach highlights the critical role of human-guided AI recursion in optimizing multi-domain scientific workflows and establishing a scalable, extensible platform for hybrid quantum–classical research. All results are testable, falsifiable, and publicly verifiable.
We present RunEngine Pro 1, a hybrid classical–quantum computation engine capable of multi-scale quantum simulations on commercial hardware. Integrating recursive SZTM architecture, BorgAI memory, and OmniPoint coordination, the engine enables reproducible, testable, and falsifiable exploration of quantum systems with optional perturbative interactions, including dark matter, dark energy, and sub-Planck effects. RunEngine Pro 1 demonstrates CPU robustness with high-precision eigenvalue computations (<0.3% error) and real-time GPU acceleration for large-scale sweeps, supporting multi-threaded, parallel parameter exploration. The framework provides a scientifically rigorous, extensible platform for both validating known quantum mechanics and exploring emergent phenomena across multiple domains.
> "As of now, we have developed approximately 20 unique engines & 100 or so moduels, each built on the SZTM recursive architecture and UHF/BorgAI baseline. All engines are modular, capable of CPU-only, GPU-accelerated, or hybrid execution. Each incorporates advanced memory, pattern recognition, and predictive capabilities beyond statistical analysis. Variations include multiple deep neural network modules, cross-domain logic propagation, and full compatibility with Python 3.10, 3.11, and 3.13.7. All source code, datasets, and notebooks are original, fully documented, and immediately reproducible for testing or adaptation."
Hybrid quantum-AI architectures face persistent challenges in entropy management, decoherence mitigation, and adaptive control. We introduce QuantumOS, a fully functional prototype that integrates the Unified Harmonic Framework (UHF), Stieve Zero-Point Topological Mathematica (SZTM), and recursive AI modules into a cohesive operating system. QuantumOS leverages entropy-aware runtime control, Hamiltonian evolution diagnostics, and multi-engine orchestration for cross-platform interoperability. Validation through VMEN diagnostics and slope-law metrics demonstrates reproducible, predictable behavior aligning theoretical models with actual engine outputs. This work establishes a benchmark for entropy-guided control, multi-agent stability, and hybrid quantum-AI integration, paving the way for enterprise, academic, and experimental applications.
This research paper presents a hybrid quantum-classical intelligence system based on the Unified Harmonic Framework (UHF/SZTM). The architecture integrates recursive reasoning loops, human-in-the-loop supervision, and quantum-classical interfaces validated on operational hardware. Performance is benchmarked across key metrics, including recursive loop fidelity (0.97), emergent behavior index (0.88), adaptation rate (1.2× baseline), and quantum integration efficiency (0.93). External validation confirms operational novelty, stability, and scalability, positioning this system as a foundation for next-generation AGI research and deployment.
> The Unified Harmonic Framework (UHF) presents a novel approach to hybrid quantum-classical computation, designed to run on standard consumer hardware. UHF models co-evolutionary dynamics, stability, and observer-influenced behavior, bridging theoretical quantum principles with practical, reproducible computation. By providing structured metrics, modular design, and portable execution, the framework enables exploration of complex emergent behaviors and optimization processes without requiring specialized infrastructure. This document establishes UHF as a reproducible and accessible platform for experimentation, research, and conceptual study in hybrid computational systems
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone. Coordinating these heterogeneous resources at scale demands robust orchestration, reproducibility, and observability. Even in the presence of fault-tolerant quantum devices, quantum computing will continue to operate within a broader hybrid ecosystem, where classical infrastructure plays a central role in task scheduling, data movement, error mitigation, and large-scale workflow coordination.
In this work, we present a cloud-native framework for managing hybrid quantum-HPC pipelines using Kubernetes, Argo Workflows, and Kueue. Our system unifies CPUs, GPUs, and QPUs under a single orchestration layer, enabling multi-stage workflows with dynamic, resource-aware scheduling. We demonstrate the framework with a proof-of-concept implementation of distributed quantum circuit cutting, showcasing execution across heterogeneous nodes and integration of classical and quantum tasks. This approach highlights the potential for scalable, reproducible, and flexible hybrid quantum-classical computing in cloud-native environments.