{"title":"走向线性斜坡QAOA协议:在解决一些组合优化问题中的缩放优势的证据","authors":"J. A. Montañez-Barrera, Kristel Michielsen","doi":"10.1038/s41534-025-01082-1","DOIUrl":null,"url":null,"abstract":"<p>The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters <span>\\({\\{{\\gamma }_{i},{\\beta }_{i}\\}}_{i = 0}^{p-1}\\)</span>. 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 <i>N</i><sub><i>q</i></sub> = 42 qubits and <i>p</i> = 400 layers suggest that the success probability scales as <span>\\(P({x}^{* })\\approx {2}^{-\\eta (p){N}_{q}+C}\\)</span>, where <i>η</i>(<i>p</i>) decreases with increasing <i>p</i>. For example, in Weighted Maxcut instances, <i>η</i>(10) = 0.22 improves to <i>η</i>(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 <i>N</i><sub><i>q</i></sub> = 109 qubits, <i>p</i> = 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.</p>","PeriodicalId":19212,"journal":{"name":"npj Quantum Information","volume":"59 1","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems\",\"authors\":\"J. A. Montañez-Barrera, Kristel Michielsen\",\"doi\":\"10.1038/s41534-025-01082-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters <span>\\\\({\\\\{{\\\\gamma }_{i},{\\\\beta }_{i}\\\\}}_{i = 0}^{p-1}\\\\)</span>. 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 <i>N</i><sub><i>q</i></sub> = 42 qubits and <i>p</i> = 400 layers suggest that the success probability scales as <span>\\\\(P({x}^{* })\\\\approx {2}^{-\\\\eta (p){N}_{q}+C}\\\\)</span>, where <i>η</i>(<i>p</i>) decreases with increasing <i>p</i>. For example, in Weighted Maxcut instances, <i>η</i>(10) = 0.22 improves to <i>η</i>(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 <i>N</i><sub><i>q</i></sub> = 109 qubits, <i>p</i> = 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.</p>\",\"PeriodicalId\":19212,\"journal\":{\"name\":\"npj Quantum Information\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2025-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Quantum Information\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1038/s41534-025-01082-1\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Quantum Information","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1038/s41534-025-01082-1","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems
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 Nq = 42 qubits and p = 400 layers suggest that the success probability scales as \(P({x}^{* })\approx {2}^{-\eta (p){N}_{q}+C}\), where η(p) decreases with increasing p. For example, in Weighted Maxcut instances, η(10) = 0.22 improves to η(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 Nq = 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.
期刊介绍:
The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.