Adaptive construction of ansatz circuits offers a promising route towards applicable variational quantum eigensolvers on near-term quantum hardware. Those algorithms aim to build up optimal circuits for a certain problem and ansatz circuits are adaptively constructed by selecting and adding entanglers from a predefined pool. In this work, we propose a way to construct entangler pools with reduced size by leveraging classical algorithms. Our method uses mutual information between the qubits in classically approximated ground state to rank and screen the entanglers. The density matrix renormalization group method is employed for classical precomputation in this work. We corroborate our method numerically on small molecules. Our numerical experiments show that a reduced entangler pool with a small portion of the original entangler pool can achieve same numerical accuracy. We believe that our method paves a new way for adaptive construction of ansatz circuits for variational quantum algorithms.

ansatz 电路的自适应构造为在近期量子硬件上适用的变分量子特征求解器提供了一条有前途的途径。这些算法旨在为某个问题建立最佳电路，并且通过从预定义的池中选择和添加纠缠器来自适应地构建 ansatz 电路。在这项工作中，我们提出了一种利用经典算法构建尺寸减小的纠缠池的方法。我们的方法使用经典近似基态中量子位之间的互信息来对纠缠器进行排序和筛选。本工作采用密度矩阵重整化群方法进行经典的预计算。我们在小分子上用数值证实了我们的方法。我们的数值实验表明，减少的纠缠池与原始纠缠池的一小部分可以达到相同的数值精度。我们相信我们的方法为变分量子算法的 ansatz 电路的自适应构建铺平了道路。