Quantum-Enhanced Memory Architectures for Graph-Based AI Systems: A Theoretical Framework with Feasibility Analysis

Quantum-Enhanced Memory Architectures for Graph-Based AI Systems: A Theoretical Framework with Feasibility Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantum-Enhanced Memory Architectures for Graph-Based AI Systems: A Theoretical Framework with Feasibility Analysis Aravind Balaji, Nik Bear Brown This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8951581/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Graph Neural Networks (GNNs) face critical computational and memory bottlenecks when scaling to large graphs. This paper proposes QEMA-G (Quantum-Enhanced Memory Architecture for Graph-based AI), a theoretical framework integrating quantum memory primitives with graph neural network computation. QEMA-G comprises four components: a QRAM-backed graph store, a quantum message-passing mechanism, a quantum graph attention module, and a hybrid classical-quantum orchestration controller. We provide rigorous complexity analysis under two regimes: an idealized setting assuming O (log N )-depth QRAM, and a realistic setting incorporating amplitude encoding overhead and NISQ-era noise acknowledging that fault-tolerant QRAM remains experimentally immature. The dual-regime analysis, which explicitly identi es both advantage and disadvantage regimes, constitutes the central contribution. Rather than proposing a near-term deployable system, we derive precise hardware target speci cations qubit count ( > 10 3 routing qubits), gate delity thresholds ( ϵ g 0), and inference break-even conditions (∼ 1.3 × 10 5 queries) that constitute actionable engineering targets for the quantum hardware community. Toy-scale Qiskit validation on 4-qubit circuits con rms protocol correctness with F = 0.94 under simulated IBM Brisbane noise conditions (not hardware execution). All speedup comparisons use consistent metrics comparing quantum circuit depth against classical sequential depth. Artificial Intelligence and Machine Learning Quantum Computing Graph Neural Networks QRAM Variational Quantum Circuits Memory Architecture NISQ Amplitude Encoding Hybrid Quantum-Classical Systems Dequantization Error Mitigation Graph Attention Knowledge Graphs Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8951581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595963019,"identity":"17722787-7342-4a19-8d57-69151507b2df","order_by":0,"name":"Aravind Balaji","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-4011-3710","institution":"Northeastern University","correspondingAuthor":true,"prefix":"","firstName":"Aravind","middleName":"","lastName":"Balaji","suffix":""},{"id":595963047,"identity":"0708b000-42d9-47ba-b2d7-e8e9f62c16a6","order_by":1,"name":"Nik Bear Brown","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Nik","middleName":"Bear","lastName":"Brown","suffix":""}],"badges":[],"createdAt":"2026-02-24 01:52:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8951581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8951581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103507764,"identity":"9fb4b90b-c031-4418-92cc-574cffdda3e6","added_by":"auto","created_at":"2026-02-26 13:44:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":630851,"visible":true,"origin":"","legend":"","description":"","filename":"QEMAGFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8951581/v1_covered_fed4af5a-fe3b-4c18-a0de-89ac54376dac.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eQuantum-Enhanced Memory Architectures for Graph-Based AI Systems: A Theoretical Framework with Feasibility Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Northeastern University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quantum Computing, Graph Neural Networks, QRAM, Variational Quantum Circuits, Memory Architecture, NISQ, Amplitude Encoding, Hybrid Quantum-Classical Systems, Dequantization, Error Mitigation, Graph Attention, Knowledge Graphs","lastPublishedDoi":"10.21203/rs.3.rs-8951581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8951581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGraph Neural Networks (GNNs) face critical computational and memory bottlenecks when scaling to large graphs. This paper proposes QEMA-G (Quantum-Enhanced Memory Architecture for Graph-based AI), a theoretical framework integrating quantum memory primitives with graph neural network computation. QEMA-G comprises four components: a QRAM-backed graph store, a quantum message-passing mechanism, a quantum graph attention module, and a hybrid classical-quantum orchestration controller. We provide rigorous complexity analysis under two regimes: an idealized setting assuming \u003cem\u003eO\u003c/em\u003e(log\u003cem\u003eN\u003c/em\u003e)-depth QRAM, and a realistic setting incorporating amplitude encoding overhead and NISQ-era noise acknowledging that fault-tolerant QRAM remains experimentally immature. The dual-regime analysis, which explicitly identi es both advantage and disadvantage regimes, constitutes the central contribution. Rather than proposing a near-term deployable system, we derive precise hardware target speci cations qubit count (\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;10\u003csup\u003e3\u003c/sup\u003e routing qubits), gate delity thresholds (\u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026lt;\u003c/em\u003e 0.0067 after topology compilation), graph density requirements (\u003cem\u003ed\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Ω(\u003cem\u003en\u003c/em\u003e\u003csup\u003e\u003cem\u003eα\u003c/em\u003e\u003c/sup\u003e), \u003cem\u003eα\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0), and inference break-even conditions (\u0026sim; 1.3 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e queries) that constitute actionable engineering targets for the quantum hardware community. Toy-scale Qiskit validation on 4-qubit circuits con rms protocol correctness with F\u0026thinsp;=\u0026thinsp;0.94 under simulated IBM Brisbane noise conditions (not hardware execution). All speedup comparisons use consistent metrics comparing quantum circuit depth against classical sequential depth.\u003c/p\u003e","manuscriptTitle":"Quantum-Enhanced Memory Architectures for Graph-Based AI Systems: A Theoretical Framework with Feasibility Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 06:28:11","doi":"10.21203/rs.3.rs-8951581/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84953795-d901-4b09-bca0-cbbdd364a218","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63413206,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-02-25T06:28:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 06:28:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8951581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8951581","identity":"rs-8951581","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}