论文标题
用于量子化学应用的变异量子算法中不同优化器的基准测试
Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry
论文作者
论文摘要
经典优化器在确定变异量子算法的准确性和收敛性方面起着至关重要的作用。在文献中，许多具有自己架构的优化器已被方便地用于不同的应用程序。在这项工作中，我们考虑了一些流行的优化器，并评估了它们在变性量子算法中的性能，以在现实的嘈杂环境中用于量子化学的应用。我们基于基于简单分子的量子模拟（例如氢，氢化锂，氢化氢，水和氟化氢）的量子模拟对优化器进行基准测试。地面能量，解离能和偶极矩的误差是用作码数的参数。所有模拟均使用理想的量子电路模拟器，嘈杂的量子电路模拟器和带有IBM开罗量子设备嵌入噪声的噪声模拟器进行，以了解理想且逼真的量子环境中经典优化器的性能。我们使用标准的统一耦合簇（UCC）ANSATZ进行模拟，量子数的数量从氢分子到氟化氢量从两个开始，从氢分子开始。基于这些优化器在理想量子电路中的性能，结合梯度（CG），有限的内存broyden-fletcher-goldcher-goldfarb-shanno Bound（L_BFGS）B）以及顺序最小二乘程序（SLSQP）优化器是最佳的基于良好的基于基于渐变的优化器。在无线性近似（Cobyla）和Powell在无梯度方法中效果最有效的优化。但是，在嘈杂的量子电路条件下，同时扰动随机近似（SPSA），Powell和Cobyla是表现最佳的优化者之一。
Classical optimizers play a crucial role in determining the accuracy and convergence of variational quantum algorithms. In literature, many optimizers, each having its own architecture, have been employed expediently for different applications. In this work, we consider a few popular optimizers and assess their performance in variational quantum algorithms for applications in quantum chemistry in a realistic noisy setting. We benchmark the optimizers with critical analysis based on quantum simulations of simple molecules, such as Hydrogen, Lithium Hydride, Beryllium Hydride, water, and Hydrogen Fluoride. The errors in the ground-state energy, dissociation energy, and dipole moment are the parameters used as yardsticks. All the simulations were carried out with an ideal quantum circuit simulator, a noisy quantum circuit simulator, and a noisy simulator with noise embedded from the IBM Cairo quantum device to understand the performance of the classical optimizers in ideal and realistic quantum environments. We used the standard unitary coupled cluster (UCC) ansatz for simulations, and the number of qubits varied from two, starting from the Hydrogen molecule to ten qubits, in Hydrogen Fluoride. Based on the performance of these optimizers in the ideal quantum circuits, the conjugate gradient (CG), limited-memory Broyden-Fletcher-Goldfarb-Shanno bound (L_BFGS)B), and sequential least squares programming (SLSQP) optimizers are found to be the best-performing gradient-based optimizers. While constrained optimization by linear approximation (COBYLA) and POWELL perform most efficiently among the gradient-free methods. However, in noisy quantum circuit conditions, Simultaneous Perturbation Stochastic Approximation (SPSA), POWELL, and COBYLA are among the best-performing optimizers.
