在重六边形图上扩展高阶等阶自旋玻璃模型的全芯片 QAOA

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Elijah Pelofske, Andreas Bärtschi, Lukasz Cincio, John Golden, Stephan Eidenbenz
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引用次数: 0

摘要

我们的研究表明,针对高阶、随机系数、重六相容自旋玻璃伊辛模型的量子近似优化算法(QAOA)在 p = 1 到 p = 5 的 16 到 127 量子比特的问题规模中具有很强的参数集中性,这使得 QAOA 角度的参数转移具有计算效率。矩阵乘积状态 (MPS) 仿真用于计算无噪声 QAOA 性能。在具有 16、27 和 127 量子位的嘈杂 IBM 量子超导处理器上,利用 JuliQAOA 工具从单个 16 量子位实例中学习的 QAOA 角度,在 100 个高阶伊辛模型的集合上执行了硬件兼容的短深度 QAOA 电路。我们发现,最好的量子处理器能找到 p = 2 或 p = 3 的较低能量解决方案,并且找到的平均能量与无噪声分布相差约 2 倍。我们使用最多 414 量子比特处理器的 NISQ 硬件网格搜索结果表明,随着问题规模的增大,p = 1 QAOA 的能量分布仍然非常相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Scaling whole-chip QAOA for higher-order ising spin glass models on heavy-hex graphs

Scaling whole-chip QAOA for higher-order ising spin glass models on heavy-hex graphs

We show that the quantum approximate optimization algorithm (QAOA) for higher-order, random coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from 16 up to 127 qubits for p = 1 up to p = 5, which allows for computationally efficient parameter transfer of QAOA angles. Matrix product state (MPS) simulation is used to compute noise-free QAOA performance. Hardware-compatible short-depth QAOA circuits are executed on ensembles of 100 higher-order Ising models on noisy IBM quantum superconducting processors with 16, 27, and 127 qubits using QAOA angles learned from a single 16-qubit instance using the JuliQAOA tool. We show that the best quantum processors find lower energy solutions up to p = 2 or p = 3, and find mean energies that are about a factor of two off from the noise-free distribution. We show that p = 1 QAOA energy landscapes remain very similar as the problem size increases using NISQ hardware gridsearches with up to a 414 qubit processor.

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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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