In this paper, we eliminate the classical outer learning loop of the quantum approximate optimization algorithm (QAOA) and present a strategy to find good parameters for QAOA based on topological arguments of the problem graph and tensor network techniques. Starting from the observation of the concentration of control parameters of QAOA, we find a way to classically infer parameters which scales polynomially in the number of qubits and exponentially with the depth of the circuit. Using this strategy, the quantum processing unit (QPU) is only needed to sample from the final state of QAOA. This method paves the way for a variation-free version of QAOA and makes QAOA more practical for applications on NISQ devices. We investigate the performance of the proposed approach for the initial assumptions and its resilience with respect to situations where they are not fulfilled. Moreover, we investigate the applicability of our method beyond the scope of QAOA, in improving schedules for q...

中文翻译：

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无需访问量子处理单元即可训练量子近似优化算法

在本文中，我们消除了量子近似优化算法（QAOA）的经典外部学习环，并提出了一种基于问题图的拓扑参数和张量网络技术为QAOA寻找良好参数的策略。从观察QAOA的控制参数集中度开始，我们找到了一种经典地推断参数的方法，该参数在量子位的数量上呈多项式缩放，并随电路的深度呈指数级增长。使用这种策略，仅需要量子处理单元（QPU）从QAOA的最终状态进行采样。此方法为无变化版本的QAOA铺平了道路，并使QAOA在NISQ设备上的应用更加实用。我们调查了最初假设的拟议方法的性能及其在未实现的情况下的弹性。此外，我们调查了我们的方法在QAOA范围之外的适用性，以改进质量检查的时间表。