想自动生成和声但又怕结果太随意？这篇论文用量子启发式加规则优化，让旋律变和弦既灵活又可控，还不用训练数据。
该论文提出一种结合量子启发式候选探索与显式规则优化的混合架构，用于从旋律自动生成和声。系统通过重叠旋律语境实现候选探索，并利用优化层提升结构连贯性、功能一致性和鲁棒性。实验使用可复现指标评估，包括结构连贯性、功能和声一致性、和声相似性和鲁棒性。结果表明，该方法在保持调性结构和终止行为的同时，允许多种有效的和声实现，且无需训练语料库。
Designing Maintainable Hybrid Generative Systems: A Quantum-Inspired Approach to Automated Music Harmony Generation
This paper presents the design and evaluation of a maintainable hybrid generative architecture for automated music harmony generation from melody. The proposed system combines quantum-inspired candidate exploration over overlapping melodic contexts with explicit rule-based optimization to balance generative flexibility and structural control. The architecture is evaluated using explicit and reproducible metrics covering structural coherence, functional agreement, harmonic similarity, and robustness. The results show that the proposed approach produces harmonizations that preserve tonal structure and cadential behavior while allowing multiple valid harmonic realizations. Furthermore, the optimization layer improves structural coherence, stability, and predictability without requiring a training corpus. The study demonstrates that transparent and controllable hybrid generative systems can be systematically designed and evaluated within the context of Information Systems Development.