[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-202159-105":59,"doc-detail-202159-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","bowtie-varqte-a-resource-efficient-quantum-state-preparation-primitive","Bowtie VarQTE - A Resource-Efficient Quantum State Preparation Primitive","","Quantum state preparation is essential for many quantum algorithms, where the quality of the initial state can dominate success probability, accuracy, and total resource cost. This work proposes a resource-efficient approximation framework for bowtie Variational Quantum Time Evolution (VarQTE). By leveraging existing causal light-cones, it minimizes quantum requirements when evaluating gradient and quantum geometric tensor terms using classical simulation for causally relevant subcircuits. Exact parameter updates follow McLachlan’s variational principle, improving numerical stability. Comparisons show comparable fidelities to an approximate quantum compilation tensor-network compiled Trotter method, without requiring a classical representation of the target state.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/bowtie-varqte-a-resource-efficient-quantum-state-preparation-primitive/202159/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/bowtie-varqte-a-resource-efficient-quantum-state-preparation-primitive/202159.png","ImageObject",300,407,{"name":92,"@type":93},"Dozel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-12","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does bowtie VarQTE address in quantum state preparation?","Question",{"text":112,"@type":113},"It targets the high cost of preparing physically structured states, where circuit depth and resource requirements can dominate algorithm complexity, by reducing quantum resource usage while approximating the desired time-evolution action.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does bowtie VarQTE reduce quantum resource requirements?",{"text":117,"@type":113},"It leverages causal light-cones to use classical simulation for causally relevant subcircuits when computing gradient and quantum geometric tensor terms, reserving quantum resources only where necessary.",{"name":119,"@type":110,"acceptedAnswer":120},"How does bowtie VarQTE compare to the tensor-network compiled Trotter (AQC) approach?",{"text":121,"@type":113},"Numerical experiments indicate bowtie VarQTE can achieve comparable fidelities without needing a classical (approximate) representation of the target state, which is a key bottleneck for the AQC method.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},202159,1790471987,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962085662650,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","arXiv :2605 .20331v1 [ quant-ph] 19 May 2026  \nBowtie VarQTE: A Resource-Efficient Quantum State Preparation Primitive  \nMarc Drudis, 1 Alberto Baiardi, 1 Mattia Chiurco, 1, 2 Francesco Tacchino, 1 Stefan Woerner, 1 and Christa Zoufal 1  \n1 IBM Quantum, IBM Research Europe — Zurich, R¨uschlikon 8803, Switzerland  \n2 Institute for Theoretical Physics, ETH Zurich, Switzerland (Dated: May 21, 2026)  \nThe preparation of quantum states is a fundamental requirement for many quantum algorithms. A native route to preparing physically structured states is based on short-time simulation of dynamical processes, such as real or imaginary time evolution. This work presents a resource-efficient framework for the approximation thereof with bowtie Variational Quantum Time Evolution (VarQTE) which uses classical simulation where possible and quantum resources where necessary. We introduce a framework that leverages existing causal light-cones to minimize quantum resource requirements in the evaluation of gradient and quantum geometric tensor terms by utilizing classical simulation methods for causally relevant subcircuits. This in turn enables exact parameter updates according to McLachlan’s variational principle and, thereby, improves numerical stability. We conduct a comparison with a state preparation method that is based on a tensor-network compiled Trotter algorithm: approximate quantum compilation (AQC) . In recent work, this approach has shown impressive performance. However, its key-bottleneck is the necessity to have a classical (approximate) representation of the target state. Our numerical experiments indicate that bowtie VarQTE can achieve comparable fidelities without this requirement. We further illustrate how bowtie VarQTE can facilitate a state-preparation pipeline that combines the simulation of imaginary and real time evolution for a sample-based quantum algorithm. In fact, results on 2D systems show how bowtie VarQTE can reduce the quantum requirements compared to standard, sample-based Krylov diagonalization calculations. Our results indicate that VarQTE is a promising primitive for the preparation of physically structured quantum states that reduces requirements on quantum resources by leveraging existing structures and the associated possibility of enabling classical simulations.  \nI. INTRODUCTION  \nEfficient quantum state preparation underpins the performance of a wide range of quantum algorithms, playing a decisive role in applications from ground state search algorithms, such as sampling-based quantum diagonalization and quantum phase estimation to Hamiltonian simulation, and even partial differential equation simulations. In many such algorithms, the success probability, accuracy, and overall resource requirements are critically dependent on the quality of the initial state, often relying on substantial overlap with specific eigenstates or low-energy subspaces of interest. Consequently, the cost of preparing these structured input states can dominate the total computational complexity, even hinder potential quantum speedups. Hence, designing scalable, robust, and hardware-efficient state preparation protocols has emerged as a central obstacle in realizing the practical potential of quantum algorithms across diverse application domains.  \nReal- and imaginary-time evolution provide a natural framework for the preparation of physically motivated quantum states by driving a quantum system toward a desired target-state manifold. The prevailing state-ofthe-art approach to simulating the time evolution of a klocal Hamiltonian is based on product-formula methods constructed via Trotterization [1–3] . For real-time evolution, such Trotter circuits are straightforward to implement and are accompanied by rigorous analytical bounds that quantify the trade-off between simulation error and circuit depth. Nevertheless, attaining high-precision evo-  \nlution typically requires circuit depths that are prohibitively large for early-gen","cbCaisD1dYQ1BC5G","https://ap.wps.com/l/cbCaisD1dYQ1BC5G","pdf",1100391,"English","# Introduction\n## Background: time-evolution-based state preparation\n## Variational Quantum Time Evolution (VarQTE)\n## Contributions and approach","[{\"question\":\"What problem does bowtie VarQTE address in quantum state preparation?\",\"answer\":\"It targets the high cost of preparing physically structured states, where circuit depth and resource requirements can dominate algorithm complexity, by reducing quantum resource usage while approximating the desired time-evolution action.\"},{\"question\":\"How does bowtie VarQTE reduce quantum resource requirements?\",\"answer\":\"It leverages causal light-cones to use classical simulation for causally relevant subcircuits when computing gradient and quantum geometric tensor terms, reserving quantum resources only where necessary.\"},{\"question\":\"How does bowtie VarQTE compare to the tensor-network compiled Trotter (AQC) approach?\",\"answer\":\"Numerical experiments indicate bowtie VarQTE can achieve comparable fidelities without needing a classical (approximate) representation of the target state, which is a key bottleneck for the AQC method.\"}]","Bowtie VarQTE - A Resource-Efficient Quantum State Preparation Primitive | PDF",1788543047,48]