Noisy Qubits, Hard Problems: A SystematicReview and Taxonomy of Quantum OptimizationBeyond Toy Benchmarks

Noisy Qubits, Hard Problems: A SystematicReview and Taxonomy of Quantum OptimizationBeyond Toy Benchmarks | 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 Noisy Qubits, Hard Problems: A SystematicReview and Taxonomy of Quantum OptimizationBeyond Toy Benchmarks Srikanth Kumar Sridhara, Dr. K. Kishore Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8721596/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 Quantum optimization has become a leading application for near-term quantumcomputing, and yet many publications compare algorithms against idealizedassumptions and small toy benchmarks. This limits the interpretability, reproducibility,and practical relevance of reported performance gains, particularlyin the noisy intermediate-scale quantum (NISQ) era. In this work, we presenta systematic literature review that investigate quantum optimization beyondtoy benchmarks. Following established SLR protocols, we analyze the literaturealong multiple methodological dimensions, including algorithmic approach,benchmark realism, encoding strategies, hybrid quantum-classical workflows,hardware and noise modeling, evaluation metrics, and reporting practices. Weintroduce a unified taxonomy that captures the interaction between problemformulation, encoding overhead, noise-aware execution, and hybrid optimizationloops. In addition, we propose a reproducibility checklist and scoring rubric toassess reporting completeness and experimental rigor across studies. Instead ofdeveloping new quantum optimization algorithms or making theoretical quantumadvantage claims, the main contribution of this work is that it providesa methodological analysis on benchmarking realism and encoding or evaluationpractices as well as reproducibility rigor in NISQ-era quantum optimization studies.Our review uncovers a continued chasm between algorithmic innovation andevaluation maturity, with the number of publications and diversity of methodsgrowing without corresponding growth in standardized benchmarks, strongclassical baselines, noise-consistent evaluation, or reproducibility. Quantum Optimization Noisy Quantum Devices NISQ QAOA Quantum Annealing Toy Benchmark Benchmarking Full Text Additional Declarations No competing interests reported. Supplementary Files SpringerNaturewithoutname.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. 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