We study how to evaluate hybrid quantum programs as end-to-end workflows rather than as isolated devices or algorithms. Building on the Hybrid Quantum Program Evaluation Framework (HQPEF), we formalize a workflow-aware Quantum Readiness Level (QRL) score; define a normalized speedup under quality constraints for the Utility of Quantumness (UQ); and provide a timing-and-drift audit for hybrid pipelines. We complement these definitions with concise Python reference implementations that illustrate how to instantiate the metrics and audit procedures with state-of-the-art classical and quantum solvers (e.g., via Qiskit or PennyLane), while preserving matched-budget discipline and reproducibility.
Quantum computing has the potential to reshape scientific research in areas like materials science, chemistry, biology, and more. But many researchers still face hurdles, especially when trying to integrate quantum tools into existing analysis pipelines. In this paper, we explore how the Galaxy Project, a platform known for enabling reproducible computational research, can be extended to support hybrid quantum-classical workflows. By combining Galaxy's user-friendly, provenance-aware environment with quantum programming tools, we offer scientists a practical way to build and share workflows that include both quantum and classical components. This GalaxyQ approach lowers the entry barrier for quantum computing and promotes transparency, collaboration, and reproducibility in scientific computing.
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.