[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83172-en":3,"doc-seo-83172-105":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},83172,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Quantum Sampling Architecture for Protein Structure Reconstruction on Utility-Scale Hardware","Predicting short-peptide structures in protein binding pockets remains challenging because it requires physics-based conformational search, yet practical search methods are limited on today’s hardware. QSAD introduces a quantum-classical framework that reformulates peptide structure prediction as amino-acid-level Hamiltonian sampling and replaces iterative optimization with non-iterative Hamiltonian evolution. Running fully on IBM Heron R2 over 101 peptides, QSAD improves accuracy by 27–71%, keeps the lowest variance across lengths, and tolerates 3–5× more noise, while reducing mean quantum execution time by 27× versus VQE.","QUANTUM SAMPLING ARCHITECTURE FOR PROTEIN STRUCTURE RECONSTRUCTION ON UTILITY-SCALE HARDWARE  \nYuqi Zhang  \nKent State University Kent, OH, USA Cleveland Clinic Cleveland, OH, USA  \nBo Fang  \nUniversity of Texas at Arlington Arlington, TX, USA  \nYuxin Yang  \nCleveland Clinic Cleveland, OH, USA  \nFeixiong Cheng  \nCleveland Clinic Cleveland, OH, USA  \narXiv :2607 .0697 1v 1 [ cs .ET] 8 Jul 2026  \nJieyang Chen  \nUniversity of Oregon Eugene, OR, USA  \nSherry Fang  \nRegailator Inc. Stow, OH, USA  \nSiwei Chen  \nUniversity of Chicago Chicago, IL, USA  \nJunhan Zhao  \nUniversity of Chicago Chicago, IL, USA  \nQiang Guan∗  \nKent State University  \nKent, OH, USA  \n[qguan@kent.edu](qguan@kent.edu)  \nJuly 9, 2026  \nABSTRACT  \nPredicting the structure of short peptides in protein binding pockets remains difficult because this regime requires physics-based conformational search, yet existing methods do not provide a practical way to carry out that search on current hardware. We present QSAD, a quantum-classical framework that reformulates peptide structure prediction as amino-acid-level Hamiltonian sampling and replaces iterative optimization with non-iterative Hamiltonian evolution. Executed entirely on IBM Heron R2 across 101 binding-pocket peptides (5–18 residues), QSAD improves prediction accuracy by 27–71% over all evaluated AI and quantum baselines while maintaining the lowest variance across tested lengths. QSAD also tolerates noise levels 3–5 × beyond typical hardware error rates, where iterative methods fail, and reduces mean quantum execution time by 27× relative to VQE. The sampled ensemble further supports approximate reconstruction of protein energy landscapes. These results establish coarse-grained quantum sampling as a practical computational path for structure prediction in regimes where data-driven methods lack sufficient signal.  \n1 Introduction  \nPredicting the three-dimensional structure of a protein from its amino-acid sequence is a central problem in computational biology, with direct importance for understanding biological function, disease mechanisms, and drug design [1] . Within this broad problem, one specific regime remains particularly difficult in practice: short peptides of 5–20 residues located in binding-pocket regions. These segments mediate interactions between proteins and drugs, substrates, or partner proteins, so that their geometry directly influences binding affinity and biological activity. They are difficult to predict because protein folding is fundamentally a process of exploring the energy landscape to identify stable conformational basins. However, such systems lack the intuitive sequence and evolutionary patterns that enable modern prediction methods, such as deep learning, to work effectively, and their energy landscapes are substantially more complex than those of protein systems that follow more general structural regularities. As a result, existing methods perform poorly in this regime [2, 3] . Figure 1 shows 6OIM, a representative pocket–ligand structure of the cancer target KRAS G12C.  \nA PREPRINT-JULY 9, 2026  \n\n|  |  |  |\n| --- | --- | --- |\n\nFigure 1: Pocket–ligand structure of 6OIM, a KRAS G12C system. This example shows the local binding-pocket region studied in this work.  \nAn effective solution must satisfy two conditions at once. First, it must derive structure by mapping the underlying energy landscape from physical principles, because short peptides do not provide enough sequence signal to support reliable statistical learning. Second, it must search an exponentially large and highly complex energy landscape efficiently enough to recover low-energy structures without exhaustive enumeration.  \nThe conformational search space in binding-pocket regions is extremely complex. Their rich biochemical characteristics give rise to a highly rugged energy landscape, significantly increasing the problem complexity. Classical methods such as molecular dynamics [4] and Monte Carlo sampling [5] t","cbCaidz4Juak5yyh","https://ap.wps.com/l/cbCaidz4Juak5yyh","pdf",4815693,4,1,18,"English","en",105,"# Introduction\n## Protein structure prediction challenges in short peptides\n## Energy landscape and limits of classical and deep learning methods\n## Quantum computing and limitations of VQE","[{\"question\":\"What problem does QSAD address in protein structure prediction?\",\"answer\":\"QSAD targets predicting structures of short peptides (about 5–20 residues) in protein binding pockets, where physics-based conformational search is needed but existing methods are not practical on current hardware.\"},{\"question\":\"How does QSAD differ from VQE-based approaches?\",\"answer\":\"QSAD reformulates prediction as amino-acid-level Hamiltonian sampling and uses non-iterative Hamiltonian evolution, avoiding the iterative optimization that makes VQE highly sensitive to quantum noise.\"},{\"question\":\"What performance and noise-robustness results are reported for QSAD on IBM Heron R2?\",\"answer\":\"On 101 binding-pocket peptides tested on IBM Heron R2, QSAD improves prediction accuracy by 27–71%, maintains the lowest variance across peptide lengths, tolerates 3–5× higher noise than typical hardware error rates, and cuts mean quantum execution time by 27× versus VQE.\"}]",1784185732,45,{"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-sampling-architecture-for-protein-structure-reconstruction-on-utility-scale-hardware","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"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":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/quantum-sampling-architecture-for-protein-structure-reconstruction-on-utility-scale-hardware/83172/",{"url":52,"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","2026-07-25","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},"What problem does QSAD address in protein structure prediction?","Question",{"text":75,"@type":76},"QSAD targets predicting structures of short peptides (about 5–20 residues) in protein binding pockets, where physics-based conformational search is needed but existing methods are not practical on current hardware.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does QSAD differ from VQE-based approaches?",{"text":80,"@type":76},"QSAD reformulates prediction as amino-acid-level Hamiltonian sampling and uses non-iterative Hamiltonian evolution, avoiding the iterative optimization that makes VQE highly sensitive to quantum noise.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and noise-robustness results are reported for QSAD on IBM Heron R2?",{"text":84,"@type":76},"On 101 binding-pocket peptides tested on IBM Heron R2, QSAD improves prediction accuracy by 27–71%, maintains the lowest variance across peptide lengths, tolerates 3–5× higher noise than typical hardware 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