| _version_ | 1866901775146024960 |
|---|---|
| author | Stieve, Jonathan |
| author_facet | Stieve, Jonathan |
| contents | <p>This work introduces the Quantum Qubit Optimization Human-in-the-Loop (HITL) Engine, a novel framework for adaptive quantum control that integrates recursive self-learning, autonomous parameter optimization, and human oversight. Unlike traditional static methods, the engine dynamically monitors qubit states, entanglement fidelity, and system coherence in real time, iteratively refining rotation angles, coupling strengths, and entanglement parameters. Early simulations on 20-qubit systems demonstrate rapid convergence, enhanced stability, and improved coherence retention. By bridging autonomous optimization with HITL compatibility, this approach offers a scalable pathway for next-generation quantum computing systems</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17775996 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Quantum Qubit Optimization HITL Engine: Recursive Self-Learning for Adaptive Quantum Control Stieve, Jonathan <p>This work introduces the Quantum Qubit Optimization Human-in-the-Loop (HITL) Engine, a novel framework for adaptive quantum control that integrates recursive self-learning, autonomous parameter optimization, and human oversight. Unlike traditional static methods, the engine dynamically monitors qubit states, entanglement fidelity, and system coherence in real time, iteratively refining rotation angles, coupling strengths, and entanglement parameters. Early simulations on 20-qubit systems demonstrate rapid convergence, enhanced stability, and improved coherence retention. By bridging autonomous optimization with HITL compatibility, this approach offers a scalable pathway for next-generation quantum computing systems</p> |
| title | Quantum Qubit Optimization HITL Engine: Recursive Self-Learning for Adaptive Quantum Control |
| url | https://doi.org/10.5281/zenodo.17775996 |