[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82885-en":3,"doc-seo-82885-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82885,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Quantum-Inspired Harmonic Decision Models A Computational Framework for Music Generation","This paper introduces a quantum-inspired computational framework for harmonic decision-making in music. Harmonization is formulated as an optimization problem inside a structured combinatorial space, where candidate chord sequences are scored under interacting musical constraints. The approach combines an interference-based harmonization stage with a classical optimization step rooted in tonal harmony. Evaluation on Autumn Leaves and It’s a Long Way to Tipperary shows reduced chord density, higher harmonic stability, and improved functional organization, while expert feedback emphasizes stylistic context and limits the assumption that added complexity always sounds more natural.","Quantum-Inspired Harmonic Decision Models: A Computational Framework for Music Generation  \nJosef Pavlíček1, Petra Pavlíčková1, Martin Molhanec2  \n1 CTU, Faculty of Information Technology, Thákurova 9, Prague 6, 160 00, Czech Republic  \n2 CTU, Faculty of Electrical Engineering, CTU, Technická 2, Prague 6, 160 00, Czech Republic  \nABSTRACT  \nThis paper introduces a quantum-inspired computational framework for harmonic decisionmaking in music. The proposed approach formulates harmonization as an optimization problem within a structured combinatorial space, where multiple candidate chord sequences are evaluated under interacting musical constraints. The model combines an interference-based harmonization stage with a classical optimization procedure grounded in tonal harmony. The quantum-inspired component enables the parallel consideration of multiple harmonic alternatives, while the classical stage refines the resulting sequences to ensure structural coherence and stylistic plausibility.  \nThe framework is evaluated on selected musical examples, including Autumn Leaves and It’sa Long Way to Tipperary. Quantitative analysis shows that the optimization stage significantly reduces chord density, increases harmonic stability, and improves functional organization. Atthe same time, expert evaluation highlights the importance of stylistic context, demonstrating that increased harmonic complexity is not always perceived as more natural. The results suggest that harmonic generation can be interpreted as a structured decision-making process ina constrained search space. The proposed approach provides a computational model that integrates domain-specific knowledge with an interference-based search mechanism.  \nAlthough preliminary, this work indicates that quantum-inspired methods may offer a useful framework for modeling complex decision processes in creative domains such as music. The proposed framework contributes to ongoing research on quantum-inspired models of cognition and decision-making in complex biological and creative systems.  \nKeywords: quantum-inspired cognition, decision-making, harmonic generation, computational creativity, tonal harmony, optimization  \n1 Introduction  \nUnderstanding how humans generate structured creative decisions remains an open question (Simon, 1972; Gigerenzer and Gaissmaier, 2011) . This question spans multiple scientific disciplines, including cognitive science, artificial intelligence, neuroscience, and music theory (Bharucha and Krumhansl, 1983). Musical composition provides a particularly suitable domain for investigating this problem, as it combines strict formal rules with a high degree of creative freedom (Krumhansl and Kessler, 1982) . Composers operate within well-defined—though often implicitly learned—theoretical frameworks such as tonal harmony, voice leading, and  \nfunctional relationships between chords (Lerdahl and Jackendoff, 1983), yet the resulting musical structures often appear as spontaneous creative insights rather than deterministic outputs of algorithmic procedures.  \nRecent advances in artificial intelligence have led to significant progress in automatic music generation (Briot et al., 2019; Huang et al., 2018). Modern systems based on deep learning and large-scale statistical models are capable of producing stylistically convincing musical outputs by learning patterns from extensive datasets. However, these approaches primarily rely on statistical imitation and probabilistic prediction. In this sense, it remains an open question to what extent such predictions can be considered “intelligent” in a human decision-making sense. As a consequence, these models tend to reproduce stylistic regularities present in training data rather than explicitly modeling the structured decision processes underlying harmonic reasoning.  \nIn contrast, human composers navigate a complex space of possible harmonic structures while respecting theoretical constraints such as tonal function, melodic c","cbCaipBW51DC8PtQ","https://ap.wps.com/l/cbCaipBW51DC8PtQ","pdf",808472,3,1,17,"English","en",105,"# Abstract\n# Introduction\n## Motivation and problem setting\n## Limits of statistical music generation\n## Musical composition as structured decision-making\n## Context-dependent aesthetic evaluation\n## Quantum-inspired models for cognition","[{\"question\":\"How is harmonic generation modeled in the proposed framework?\",\"answer\":\"Harmonization is posed as an optimization problem over a structured combinatorial space of chord sequences evaluated under interacting musical constraints.\"},{\"question\":\"What roles do the quantum-inspired and classical stages play?\",\"answer\":\"The quantum-inspired component enables parallel consideration of multiple harmonic alternatives using an interference-based harmonization stage, while the classical stage refines the resulting sequences via optimization grounded in tonal harmony.\"},{\"question\":\"What metrics and observations are reported from the evaluation?\",\"answer\":\"Quantitative analysis shows reduced chord density, increased harmonic stability, and better functional organization, and expert evaluation highlights that stylistic context affects perceived naturalness of harmonic complexity.\"}]",1784183658,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-inspired-harmonic-decision-models-a-computational-framework-for-music-generation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/quantum-inspired-harmonic-decision-models-a-computational-framework-for-music-generation/82885/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is harmonic generation modeled in the proposed framework?","Question",{"text":75,"@type":76},"Harmonization is posed as an optimization problem over a structured combinatorial space of chord sequences evaluated under interacting musical constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What roles do the quantum-inspired and classical stages play?",{"text":80,"@type":76},"The quantum-inspired component enables parallel consideration of multiple harmonic alternatives using an interference-based harmonization stage, while the classical stage refines the resulting sequences via optimization grounded in tonal harmony.",{"name":82,"@type":73,"acceptedAnswer":83},"What metrics and observations are reported from the evaluation?",{"text":84,"@type":76},"Quantitative analysis shows reduced chord density, increased harmonic stability, and better functional organization, and expert evaluation highlights that stylistic context affects perceived naturalness of harmonic 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