[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85744-en":3,"doc-seo-85744-105":29,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85744,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Benchmarking Zero-Setup Quantum Circuit Simulators","Benchmarking study of GPU-accelerated approximate quantum circuit simulation in zero-setup hosted environments, comparing matrix product states (MPS) and Pauli path simulation (PPS). BlueQubit is benchmarked against AWS Braket, Quantum Rings, PPSQiskit, and PauliPropagation.jl using standardized circuit families, parameter sweeps, and statistical protocols. Results show sub-quadratic GPU bond-dimension scaling for MPS with increasing GPU advantages, while PPS on IBM’s 127-qubit kicked Ising achieves up to 1,400× speedup at fine truncation thresholds and reaches accuracy regimes inaccessible to evaluated CPU backends. A public GitHub repository provides reproducible code and configurations.","Benchmarking Zero-Setup Quantum Circuit  \nSimulators  \nArul Rhik Mazumder 1,2 , Mohammed Zuhair Mullath 1 , Hayk Tepanyan 1  \n1BlueQubit 2 Carnegie Mellon University  \n{rhik, zuhair, [hayk](hayk}@bluequbit.io)[}](hayk}@bluequbit.io)[@bluequbit.io](hayk}@bluequbit.io) [arulm@andrew.cmu.edu](arulm@andrew.cmu.edu)  \narXiv :2607 .09882v1 [ quant-ph] 10 Jul 2026  \nAbstract—Practitioners increasingly rely on hosted simulation environments, but their performance characteristics remain poorly documented. We present a systematic benchmarking study of GPU-accelerated approximate quantum simulation across two widely used methods: matrix product states (MPS) and Pauli path simulation (PPS), comparing BlueQubit (a hosted tool that handles hardware provisioning, simulator configuration, and job orchestration) against AWS Braket, Quantum Rings, PPSQiskit, and PauliPropagation.jl. For MPS, we find that GPU runtime yields sub-quadratic scaling with bond dimension, with a growing advantage over CPU at increasing scale. For Pauli path simulation on IBM’s 127-qubit kicked Ising benchmark, GPUs deliver up to 1 ,400 × speedup at fine truncation thresholds (δ = 2 .5 × 10 −5, 27.6M Pauli terms), and are the only backends that reach accuracy regimes below δ = 10 −5, which remained inaccessible to the commodity CPU-based implementations and self-contained SDKs evaluated here. We also provide a reproducible characterization of these simulators across regimes, including tradeoffs that isolated evaluations do not show. To support transparency and reuse, we provide a public GitHub repository containing all benchmarking code and configurations.  \nIndex Terms—quantum circuit simulation, GPU acceleration, matrix product state, Pauli path simulation, bond dimension scaling, performance modeling, benchmarking, high performance computing, reproducibility.  \nI. INTRODUCTION  \nClassical simulation of quantum circuits is indispensable for algorithm development, hardware validation, and establishing baselines for quantum advantage claims [1] . As quantum processors scale to hundreds of qubits (IBM’s 127-qubit Eagle, Google’s 105-qubit Willow and 53-qubit Sycamore, and Quantinuum’s 56-qubit H2 and 98-qubit Helios), the practical limits of exact state-vector simulation (O(2n ) for n qubits) force a transition to approximate methods.  \nIn parallel, the ecosystem for running these simulations has shifted. Rather than installing simulation libraries, GPU drivers, and CUDA toolkits on local hardware, practitioners increasingly turn to zero-setup simulators: hosted platforms and cloud-accessible backends that allow users to submit circuits and retrieve results without managing any local infrastructure. Services such as BlueQubit, AWS Braket [2], and Quantum Rings [3] exemplify this model, as do locally installable but self-contained packages like PPS-Qiskit [4] and PauliPropagation.jl [5] .  \nDespite the growing reliance on these platforms, no systematic study has compared their end-to-end performance across the two dominant approximate simulation methods, matrix  \nproduct states (MPS) and Pauli path simulation (PPS), under controlled conditions. Existing benchmarks focus primarily on exact state-vector simulation [6], [7], and vendor-reported numbers typically cover isolated operations or single circuits rather than systematic sweeps across problem parameters. This gap leaves practitioners without the information needed to select the right backend for a given task.  \nScope and goals. This paper provides a standardized, reproducible benchmarking study of zero-setup quantum circuit simulators. We evaluate BlueQubit (CPU and GPU backends for state-vector, MPS, and PPS), AWS Braket SV1, Quantum Rings, PPS-Qiskit, and PauliPropagation.jl across a common set of circuit families, parameter sweeps, and statistical protocols. Our primary goals are:  \n1) Cross-platform comparison: Identify which simulators are fastest in which regimes, using the same circuits and metrics throughout","cbCailRPHTO8T8JK","https://ap.wps.com/l/cbCailRPHTO8T8JK","pdf",2297079,1,12,"English","en",105,"# Introduction\n# Background\n## Zero-Setup Quantum Circuit Simulators\n# Benchmarking Scope and Goals\n# Key Results","[{\"question\":\"What does “zero-setup” mean in the benchmarking study?\",\"answer\":\"Zero-setup simulators can be used without installing GPU drivers, CUDA toolkits, or specialized simulation libraries on local hardware, relying instead on hosted/cloud-accessible backends or self-contained packages.\"},{\"question\":\"Which two approximate simulation methods are compared?\",\"answer\":\"The study compares matrix product states (MPS) and Pauli path simulation (PPS) across multiple platforms using controlled, reproducible protocols.\"},{\"question\":\"What are the main performance findings for MPS and PPS on GPUs?\",\"answer\":\"For MPS, GPU runtime shows sub-quadratic scaling with bond dimension and increasing advantage at larger scales, while low-entanglement cases can suffer from GPU overhead. For PPS, GPU backends deliver up to 1,400× speedup on IBM’s 127-qubit kicked Ising benchmark at fine truncation thresholds and are the only tested backends reaching accuracy regimes beyond the commodity CPU implementations.\"}]",1784205977,30,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"benchmarking-zero-setup-quantum-circuit-simulators","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/benchmarking-zero-setup-quantum-circuit-simulators/85744/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","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 does “zero-setup” mean in the benchmarking study?","Question",{"text":75,"@type":76},"Zero-setup simulators can be used without installing GPU drivers, CUDA toolkits, or specialized simulation libraries on local hardware, relying instead on hosted/cloud-accessible backends or self-contained packages.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which two approximate simulation methods are compared?",{"text":80,"@type":76},"The study compares matrix product states (MPS) and Pauli path simulation (PPS) across multiple platforms using controlled, reproducible protocols.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main performance findings for MPS and PPS on GPUs?",{"text":84,"@type":76},"For MPS, GPU runtime shows sub-quadratic scaling with bond dimension and increasing advantage at larger scales, while low-entanglement cases can suffer from GPU overhead. For PPS, GPU backends deliver up to 1,400× speedup on IBM’s 127-qubit kicked Ising benchmark at fine truncation thresholds and are the only tested backends reaching accuracy regimes beyond the commodity CPU implementations.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":28,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]