发表机构
USRA Research Institute for Advanced Computer Science (RIACS); The Hartree Centre, STFC; IBM Quantum, IBM Research Europe – Ehningen; IBM Quantum, IBM Research Europe – Zurich（美国大学空间研究协会先进计算机科学研究所; 哈特里中心，科学和技术设施委员会; IBM量子，IBM欧洲研究院-埃宁根; IBM量子，IBM欧洲研究院-苏黎世）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对硬件噪声和有限量子比特数阻碍组合优化量子优势实现的问题，提出噪声导向自适应热启动方法，利用位翻转规范变换，在100比特伊辛哈密顿量实验中高性能实现量子优化，优于非规范变换迭代热启动，为后续增强奠定基础。
AI 中文摘要
利用已知启发式方法实现量子优势以解决组合优化问题，受硬件噪声和有限量子比特数阻碍。我们提出噪声感知自适应方法——噪声导向自适应热启动（ND - AWS），基于热启动量子近似优化算法（Warm - Start QAOA）和噪声导向自适应重映射等概念。通过利用位翻转规范变换，算法利用类似振幅阻尼的噪声分量。在100比特伊辛哈密顿量上实验实现高性能量子优化近似算法，表明ND - AWS通常在无额外电路成本下优于非规范变换的迭代热启动变体，为未来增强如自适应偏置调度及与经典求解器集成奠定基础。
英文摘要
Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-aware adaptive approach to quantum approximate optimization, Noise-Directed Adaptive Warm-Starting (ND-AWS), that builds on recent concepts such as Warm-Start QAOA and Noise-Directed Adaptive Remapping. By leveraging bitflip gauge transformations, our algorithm exploits amplitude-damping-like noise components. We experimentally implement high-performance quantum optimization ansätze on 100-qubit Ising Hamiltonians, showing that ND-AWS generally improves the performance over a non-gauge-transformed iterative Warm-Starting variant, at no additional circuit cost. This places our results among the highest-quality demonstrations of quantum optimization with similar ansätze at this scale. Crucially, the simplicity of the framework opens the door for future enhancements such as adaptive bias schedules, and integration with classical solvers.
Comments8+13 pages; 3+4 figures; v2: minor implementation details corrections, fixed typos, corrected run selection in Fig. 2c; comments and suggestions are welcome!; accompanying repo: https://github.com/usra-riacs/quantum-approximate-optimization
