标题： 量子近似优化算法：性能、机制及在近期设备上的实现 显示英文标题

标题： Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices

摘要： 量子近似优化算法（QAOA）是一种混合量子-经典变分算法，旨在解决组合优化问题。 尽管其在短期量子应用中具有前景，但目前对QAOA在最低深度变体之外的性能了解甚少。 理解并部署QAOA的一个关键但缺失的要素是进行外层经典优化的构造性方法。 我们通过开发一种高效的参数优化程序并揭示其利用非绝热操作的能力，深入研究了QAOA在MaxCut问题上的性能。 基于对最优参数中观察到的模式，我们提出了启发式策略来初始化优化，以在$O(\text{poly}(p))$时间内找到准最优的$p$-级QAOA参数，而标准的随机初始化策略需要$2^{O(p)}$次优化运行才能达到类似性能。 随后，我们对QAOA进行了基准测试，并将其与量子退火进行比较，特别是在由于光谱间隙较小而导致绝热量子退火失败的困难实例上。 比较结果表明，QAOA可以通过优化学习利用非绝热机制，从而克服消失光谱间隙相关的挑战。 最后，我们提供了QAOA实验实现的现实资源分析。 当考虑测量中的量子涨落时，我们说明优化仅在超出数值模拟的问题规模下才重要，但在短期内设备上是可访问的。 我们提出了一种在二维中性原子系统中实现大规模MaxCut问题的可行方案，该系统包含几百个顶点，达到了挑战最佳经典算法的区域。 显示英文摘要

摘要： The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle combinatorial optimization problems. Despite its promise for near-term quantum applications, not much is currently understood about QAOA's performance beyond its lowest-depth variant. An essential but missing ingredient for understanding and deploying QAOA is a constructive approach to carry out the outer-loop classical optimization. We provide an in-depth study of the performance of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and revealing its ability to exploit non-adiabatic operations. Building on observed patterns in optimal parameters, we propose heuristic strategies for initializing optimizations to find quasi-optimal $p$-level QAOA parameters in $O(\text{poly}(p))$ time, whereas the standard strategy of random initialization requires $2^{O(p)}$ optimization runs to achieve similar performance. We then benchmark QAOA and compare it with quantum annealing, especially on difficult instances where adiabatic quantum annealing fails due to small spectral gaps. The comparison reveals that QAOA can learn via optimization to utilize non-adiabatic mechanisms to circumvent the challenges associated with vanishing spectral gaps. Finally, we provide a realistic resource analysis on the experimental implementation of QAOA. When quantum fluctuations in measurements are accounted for, we illustrate that optimization will be important only for problem sizes beyond numerical simulations, but accessible on near-term devices. We propose a feasible implementation of large MaxCut problems with a few hundred vertices in a system of 2D neutral atoms, reaching the regime to challenge the best classical algorithms.