标题： 带有囚禁离子量子模拟器的长程伊辛模型的量子近似优化 显示英文标题

标题： Quantum Approximate Optimization of the Long-Range Ising Model with a Trapped-Ion Quantum Simulator

摘要： 量子计算机和模拟器可能相对于经典设备具有显著优势，为量子多体系统提供见解，并可能改善解决指数级困难问题的性能，例如优化和可满足性问题。 在这里，我们报告了使用模拟量子模拟器实现的低深度量子近似优化算法（QAOA）。 我们估算了具有可调范围的长程相互作用的横向场伊辛模型的基态能量，并通过高保真、单次测量的单独量子比特采样优化了相应的组合经典问题。 我们通过变分参数的穷举搜索和闭环优化执行该算法，最多使用40个捕获离子量子比特近似基态能量。 我们通过与系统大小多项式相关的引导启发式方法对实验进行基准测试。 我们观察到，与数值结果一致，随着系统规模的扩大，QAOA性能没有显著下降，且运行时间大致与量子比特数量无关。 最后，我们对系统中出现的错误进行了全面分析，这是将QAOA应用于更一般问题实例的前进道路上的关键步骤。 显示英文摘要

摘要： Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground state energy of the Transverse Field Ising Model with long-range interactions with tunable range and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size, and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward towards the application of the QAOA to more general problem instances.