Q-CVaR-Fold: A Quantum CVaR Contrastive LearningFramework for Protein-Folding Decoy Scoring

Q-CVaR-Fold: A Quantum CVaR Contrastive LearningFramework for Protein-Folding Decoy Scoring | 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 Q-CVaR-Fold: A Quantum CVaR Contrastive LearningFramework for Protein-Folding Decoy Scoring Parham Ghayour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8302298/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 Protein structure prediction and decoy ranking remain central challenges in computational biophysics. Classical scoring functions often struggle todiscriminate near-native conformations from large populations of plausible decoys,particularly in the critical low-energy tail of the conformational distribution. Weintroduce Q-CVaR-Fold, a hybrid quantum–classical architecture that integratesa geometric graph neural network encoder with a small parameterized quantumcircuit acting as a nonlinear scoring head. To focus optimization on near-nativeconformations, we combine contrastive ranking with Conditional Value-at-Risk(CVaR) tail reweighting, yielding a risk-sensitive training objective aligned withstructural evaluation metrics.Despite using only four qubits and shallow entangling layers, Q-CVaR-Fold exhibits stable end-to-end training and avoids barren plateaus. On a decoy-rankingbenchmark, the model achieves a ROC-AUC of 0.984 and perfect top-5 nativeenrichment across all sequences, outperforming classical baselines of comparablesize. The score distributions and monotonic reduction of CVaR loss demonstratethat quantum feature transformations, coupled with tail-focused optimization, provide discriminative power beyond standard MLP heads. To our knowledge, this isthe first demonstration of a quantum-enhanced, risk-sensitive scoring model thatachieves near-perfect recovery of native structures in decoy-ranking tasks.Q-CVaR-Fold highlights the potential of hybrid quantum models for energylandscape modeling, fragment selection, and structural refinement, and offers apromising foundation for next-generation quantum–geometric methods in computational structural biology. Quantum machine learning Protein folding Decoy scoring Conditional Value-at-Risk (CVaR) Contrastive learning Parameterized quantum circuits Full Text Additional Declarations No competing interests reported. Supplementary Files quantumproteinfoldingnew.pdf 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-8302298","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557241288,"identity":"05bbe708-d92c-48ea-a419-d11dc2bf6a48","order_by":0,"name":"Parham Ghayour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABQ0lEQVRIie3RsWrCQBjA8YMrTpGuVyK5JxAuHASk4GN06XKh4C2RCoHi4BAQ4tJ2TsnQV6gIzoFAugRnIaXYxflEECepdw3FVDt063D/4XKB+91HCAA63X+NlU+oVvT1YsklWfyFUEXY8elKFeIGh7cd1Bw9pGLRT2+BOVyue4MpwNfDJVqF7/z5Kv3YT2lbzaBCnHzWQSxPW0Ejo3GUFcB+y5yLp9DvjosO2ZMb6iRVMvcc4IYpAYhRaNT2JGKOWQ9ZdxwzSRJ3ekSocHeS8DU0dpLwjSTcjrn4hRDkBpJ4FNbDAmDkqSkMm97pKXnuIJZxUmvkPqw/FgZBnt+KZsx+Mb1ewsjxt7ze09V2cEnOzdEEGpvCwhEfz8UdwzjmEyH6besH+a5W/g6DJOBM7Yk6SU4fV5UE4ABAUW50Op1OJ/sEKM97VF9810MAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Parham","middleName":"","lastName":"Ghayour","suffix":""}],"badges":[],"createdAt":"2025-12-08 00:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8302298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8302298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98284972,"identity":"233caf2e-7e90-4a20-bbe2-46035ac73849","added_by":"auto","created_at":"2025-12-16 06:48:35","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3821,"visible":true,"origin":"","legend":"","description":"","filename":"826e706a5e31488893cadc0ee2f98700.json","url":"https://assets-eu.researchsquare.com/files/rs-8302298/v1/3606e1c56420feec76089a22.json"},{"id":106752299,"identity":"feb504e7-d90a-4fe7-bc18-1983e5f812d8","added_by":"auto","created_at":"2026-04-13 07:13:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":407157,"visible":true,"origin":"","legend":"","description":"","filename":"quantumproteinfoldingnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8302298/v1_covered_5e0bc940-c1cb-489f-943d-6be416b31c9a.pdf"},{"id":98284973,"identity":"5882eba1-0647-4723-a8a3-2ffbcae1b48e","added_by":"auto","created_at":"2025-12-16 06:48:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":370260,"visible":true,"origin":"","legend":"","description":"","filename":"quantumproteinfoldingnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8302298/v1/7863e5b12c89fd8878658f5c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Q-CVaR-Fold: A Quantum CVaR Contrastive LearningFramework for Protein-Folding Decoy Scoring","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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 machine learning, Protein folding, Decoy scoring, Conditional Value-at-Risk (CVaR), Contrastive learning, Parameterized quantum circuits","lastPublishedDoi":"10.21203/rs.3.rs-8302298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8302298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Protein structure prediction and decoy ranking remain central challenges in computational biophysics. Classical scoring functions often struggle todiscriminate near-native conformations from large populations of plausible decoys,particularly in the critical low-energy tail of the conformational distribution. Weintroduce Q-CVaR-Fold, a hybrid quantum–classical architecture that integratesa geometric graph neural network encoder with a small parameterized quantumcircuit acting as a nonlinear scoring head. To focus optimization on near-nativeconformations, we combine contrastive ranking with Conditional Value-at-Risk(CVaR) tail reweighting, yielding a risk-sensitive training objective aligned withstructural evaluation metrics.Despite using only four qubits and shallow entangling layers, Q-CVaR-Fold exhibits stable end-to-end training and avoids barren plateaus. On a decoy-rankingbenchmark, the model achieves a ROC-AUC of 0.984 and perfect top-5 nativeenrichment across all sequences, outperforming classical baselines of comparablesize. The score distributions and monotonic reduction of CVaR loss demonstratethat quantum feature transformations, coupled with tail-focused optimization, provide discriminative power beyond standard MLP heads. To our knowledge, this isthe first demonstration of a quantum-enhanced, risk-sensitive scoring model thatachieves near-perfect recovery of native structures in decoy-ranking tasks.Q-CVaR-Fold highlights the potential of hybrid quantum models for energylandscape modeling, fragment selection, and structural refinement, and offers apromising foundation for next-generation quantum–geometric methods in computational structural biology.","manuscriptTitle":"Q-CVaR-Fold: A Quantum CVaR Contrastive LearningFramework for Protein-Folding Decoy Scoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 06:48:30","doi":"10.21203/rs.3.rs-8302298/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":"4b4488ae-21a6-47be-92e4-d5e30fc294ab","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T07:13:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 06:48:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8302298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8302298","identity":"rs-8302298","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}