论文标题
一种自适应量子近似优化算法，用于解决量子计算机上的组合问题
An adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
论文作者
论文摘要
量子近似优化算法（QAOA）是一种解决组合优化问题的混合变异量子古典算法。尽管有证据表明标准Qaoa Ansatz的固定形式不是最佳的，但没有系统的方法可以找到更好的Ansätze。我们通过开发QAOA的迭代版本来解决这个问题，该版本是问题的，并且也可以适用于特定的硬件约束。我们在一类最大切割图问题上模拟算法，并表明其收敛速度比标准QAOA快得多，同时减少了所需数量的CNOT门和优化参数。我们提供证据表明这种加速与绝热性的快捷方式有关。
The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems. While there is evidence suggesting that the fixed form of the standard QAOA ansatz is not optimal, there is no systematic approach for finding better ansätze. We address this problem by developing an iterative version of QAOA that is problem-tailored, and which can also be adapted to specific hardware constraints. We simulate the algorithm on a class of Max-Cut graph problems and show that it converges much faster than the standard QAOA, while simultaneously reducing the required number of CNOT gates and optimization parameters. We provide evidence that this speedup is connected to the concept of shortcuts to adiabaticity.
