{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-a-universal-qaoa-protocol-evidence-of","title":"Towards a universal QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems","arxiv_id":"2405.09169","date":"2024-05-15","proceeding":null,"authors":["J. A. Montanez-Barrera","Kristel Michielsen"],"abstract":"The Quantum Approximate Optimization Algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $\\{\\gamma_i, \\beta_i\\}_{i=0}^{p-1}$. While most prior work has focused on classically optimizing these parameters, we demonstrate that fixed linear ramp schedules, linear ramp QAOA (LR-QAOA), can efficiently approximate optimal solutions across diverse COPs. Simulations with up to $N_q=42$ qubits and $p=400$ layers suggest that the success probability scales as $P(x^*) \\approx 2^{-\\eta(p) N_q + C}$, where $\\eta(p)$ decreases with increasing $p$. For example, in Weighted Maxcut instances, $\\eta(10) = 0.22$ improves to $\\eta(100) = 0.05$. Comparisons with classical algorithms, including simulated annealing, Tabu Search, and branch-and-bound, show a scaling advantage for LR-QAOA. We show results of LR-QAOA on multiple QPUs (IonQ, Quantinuum, IBM) with up to $N_q = 109$ qubits, $p=100$, and circuits requiring 21,200 CNOT gates. Finally, we present a noise model based on two-qubit gate counts that accurately reproduces the experimental behavior of LR-QAOA.","url_abs":"https://arxiv.org/abs/2405.09169v2","url_pdf":"https://arxiv.org/pdf/2405.09169v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"towards-a-universal-qaoa-protocol-evidence-of","repo_url":"https://github.com/alejomonbar/LR-QAOA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"towards-a-universal-qaoa-protocol-evidence-of","repo_url":"https://github.com/alejomonbar/LR-QAOA-QPU-Benchmarking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}