Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems

IF 8.3 1区 物理与天体物理 Q1 PHYSICS, APPLIED
J. A. Montañez-Barrera, Kristel Michielsen
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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 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.

Abstract Image

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
量子近似优化算法(QAOA)是求解组合优化问题(cop)的一种很有前途的算法,其性能受变分参数的控制\({\{{\gamma }_{i},{\beta }_{i}\}}_{i = 0}^{p-1}\)。虽然大多数先前的工作都集中在经典优化这些参数上,但我们证明了固定的线性斜坡调度,线性斜坡QAOA (LR-QAOA)可以有效地近似不同cop的最优解。Nq = 42量子位和p = 400层的模拟表明,成功概率尺度为\(P({x}^{* })\approx {2}^{-\eta (p){N}_{q}+C}\),其中η(p)随着p的增加而减小。例如,在加权Maxcut实例中,η(10) = 0.22提高到η(100) = 0.05。与经典算法(包括模拟退火、禁忌搜索和分支定界)的比较表明,LR-QAOA具有缩放优势。我们展示了LR-QAOA在多个qpu (IonQ, quantum, IBM)上的结果,最高Nq = 109量子位,p = 100,电路需要21,200个CNOT门。最后,我们提出了一个基于双量子比特门计数的噪声模型,该模型准确地再现了LR-QAOA的实验行为。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
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.
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