Approximating Maximum Independent Set on Rydberg Atom Arrays Using Local Detunings

IF 4.3 Q1 OPTICS Advanced quantum technologies Pub Date : 2024-09-26 DOI:10.1002/qute.202400291
Hyeonjun Yeo, Ha Eum Kim, Kabgyun Jeong
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Abstract

Rydberg atom arrays operated by a quantum adiabatic principle are among the most promising quantum simulating platforms due to their scalability and long coherence time. From the perspective of combinatorial optimization, they offer an efficient solution for an intrinsic maximum independent set problem because of the resemblance between the Rydberg Hamiltonian and the cost function of the maximum independent set problem. In this study, a strategy is suggested to approximate maximum independent sets by adjusting local detunings on the Rydberg Hamiltonian according to each vertex's vertex support, which is a quantity that represents connectivity between vertices. By doing so, the strategy successfully reduces the error rate three times for the checkerboard graphs with defects when the adiabaticity is sufficient. In addition, the strategy decreases the error rate for random graphs even when the adiabaticity is relatively insufficient. Moreover, it is shown that the strategy helps to prepare a quantum many-body ground state by raising the fidelity between the evolved quantum state and a 2D cat state on a square lattice. Finally, the strategy is combined with the non-abelian adiabatic mixing and this approach is highly successful in finding maximum independent sets compared to the conventional adiabatic evolution with local detunings.

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利用局部调谐近似雷德贝格原子阵列上的最大独立集
基于量子绝热原理的里德伯原子阵列由于其可扩展性和较长的相干时间是最有前途的量子模拟平台之一。从组合优化的角度出发,由于Rydberg哈密顿量与最大独立集问题的代价函数的相似性,它们为内在最大独立集问题提供了一种有效的解。在本研究中,提出了一种策略,通过根据每个顶点的顶点支持度(顶点支持度是表示顶点之间连通性的数量)调整Rydberg hamilton上的局部失谐来近似最大独立集。通过这样做,当绝热度足够时,该策略成功地将具有缺陷的棋盘图的错误率降低了三倍。此外,即使在绝热性相对不足的情况下,该策略也降低了随机图的错误率。此外,该策略通过提高进化量子态与正方形晶格上二维猫态之间的保真度,有助于制备量子多体基态。最后,将该策略与非阿贝尔绝热混合相结合,与传统的局部失谐绝热演化相比,该方法在寻找最大独立集方面非常成功。
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7.90
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期刊最新文献
Issue Information (Adv. Quantum Technol. 12/2025) Inside Front Cover: Quantum-Enhanced Simulated Annealing Using Rydberg Atoms (Adv. Quantum Technol. 12/2025) Inside Back Cover: Method for Noise-Induced Regularization in Quantum Neural Networks (Adv. Quantum Technol. 12/2025) Back Cover: Quantum-Noise-Driven Generative Diffusion Models (Adv. Quantum Technol. 12/2025) Front Cover: Intelligent Generative Models for Quantum Neural Networks (Adv. Quantum Technol. 12/2025)
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