论文标题
QAOA几乎没有测量
The QAOA with Few Measurements
论文作者
论文摘要
量子近似优化算法（QAOA）最初是为了解决组合优化问题而开发的，但已成为评估量子计算机性能的标准。对于大量量子位（$ n \ gtrsim 10 $）而言，完全描述性的基准测定技术通常非常昂贵，因此QAOA通常在实践中用作计算基准。 QAOA涉及一个经典的优化子例程，该子例程试图找到量子子例程的最佳参数。不幸的是，许多用于QAOA的优化器需要每点的参数空间（$ n \ gtrsim 1000 $），以获得可靠的估计值。但是，一些实验性量子计算平台（例如中性原子量子计算机）的重复率较慢，在这些系统中使用QAOA中使用的经典优化子例程提出了独特的要求。在本文中，我们研究了QAOA的两种无梯度经典优化器的性能 - 双重退火和自然进化策略 - 并证明即使使用$ n = 1 $和$ n = 16 $，即使使用$ n = 1 $，也可以进行优化。
The Quantum Approximate Optimization Algorithm (QAOA) was originally developed to solve combinatorial optimization problems, but has become a standard for assessing the performance of quantum computers. Fully descriptive benchmarking techniques are often prohibitively expensive for large numbers of qubits ($n \gtrsim 10$), so the QAOA often serves in practice as a computational benchmark. The QAOA involves a classical optimization subroutine that attempts to find optimal parameters for a quantum subroutine. Unfortunately, many optimizers used for the QAOA require many shots ($N \gtrsim 1000$) per point in parameter space to get a reliable estimate of the energy being minimized. However, some experimental quantum computing platforms such as neutral atom quantum computers have slow repetition rates, placing unique requirements on the classical optimization subroutine used in the QAOA in these systems. In this paper we investigate the performance of two choices of gradient-free classical optimizer for the QAOA - dual annealing and natural evolution strategies - and demonstrate that optimization is possible even with $N=1$ and $n=16$.
