Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA). Our simulator is designed with the goal of reducing the computational cost of QAOA parameter optimization and supports both CPU and GPU execution. Our central observation is that the computational cost of both simulating the QAOA state and computing the QAOA objective to be optimized can be reduced by precomputing the diagonal Hamiltonian encoding the problem. We reduce the time for a typical QAOA parameter optimization by eleven times for $n = 26$ qubits compared to a state-of-the-art GPU quantum circuit simulator based on cuQuantum. Our simulator is available on GitHub: https://github.com/jpmorganchase/QOKit

翻译：在高保真度且拥有大量量子比特的量子计算机普及之前，经典模拟仍然是算法设计、调优和验证的重要工具。我们提出了一种用于量子近似优化算法（QAOA）的模拟器。该模拟器旨在降低QAOA参数优化的计算成本，并支持CPU和GPU执行。我们的核心发现是：通过预计算编码问题的对角哈密顿量，可以降低模拟QAOA状态和计算待优化QAOA目标函数的计算成本。与基于cuQuantum的最先进GPU量子电路模拟器相比，我们将$n=26$量子比特的典型QAOA参数优化时间缩短了十一倍。我们的模拟器已在GitHub上开源：https://github.com/jpmorganchase/QOKit