Quantum-Enhanced Graph Neural Networks for Complex Decision Making in Cognitive Autonomous Systems

Quantum-Enhanced Graph Neural Networks for Complex Decision Making in Cognitive Autonomous Systems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 16 March 2026 V1 Latest version Share on Quantum-Enhanced Graph Neural Networks for Complex Decision Making in Cognitive Autonomous Systems Author : Dr Sai Krishna Thota 0009-0008-5246-9421 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177368940.04003466/v1 93 views 38 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Graph Neural Networks (GNNs) are now one of the key tools to model relational and non-Euclidean data in multiagent systems, infrastructure networks and even molecular modeling applications. Nevertheless, in very dense or noisy graph topologies, classical message-passing architectures may experience scalability and representational degeneration, especially due to over-smoothing and insufficient higher-order dependency information. The recent developments in quantum machine learning indicate that hybrid quantum-classical models can also provide an alternative feature representation by projecting relational data to parameterized quantum circuits. This article presents a Quantum-Enhanced Graph Neural Network (QGNN) architecture that extends variational quantum circuits with graph learning pipelines, based on classical learning. Parameters are represented as parameterized qubits, and relational dependencies are represented as structured entanglement mappings. We benchmark the regimes where quantum-enhanced relational encoding proves to be more robust and stable through the swarm coordination, molecular property prediction, and financial transaction network. We also elaborate on the issue of scalability, hardware issues, and practical aspects of near-term hybrid implementations and give a critical view of the changing role of quantum methods in computational intelligence systems. Supplementary Material File (quantum enhanced graph neural networks for complex decision making.pdf) Download 290.67 KB Information & Authors Information Version history V1 Version 1 16 March 2026 Copyright This work is licensed under a Creative Commons PublicDomain Zero 1.0 Universal License Keywords autonomous systems cognitive computing computational intelligence decision making graph neural networks hilbert space multi-agent systems quantum machine learning quantum programming variational quantum circuits Authors Affiliations Dr Sai Krishna Thota 0009-0008-5246-9421 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 93 views 38 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dr Sai Krishna Thota. Quantum-Enhanced Graph Neural Networks for Complex Decision Making in Cognitive Autonomous Systems. Authorea . 16 March 2026. DOI: https://doi.org/10.22541/au.177368940.04003466/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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