This paper proposes a quantum approximate optimization algorithm (QAOA) method for wireless scheduling problems. The QAOA is one of the promising hybrid quantum-classical algorithms for many applications and it provides highly accurate optimization solutions in NP-hard problems. QAOA maps the given problems into Hilbert spaces, and then it generates Hamiltonian for the given objectives and constraints. Then, QAOA finds proper parameters from classical optimization approaches in order to optimize the expectation value of generated Hamiltonian. Based on the parameters, the optimal solution to the given problem can be obtained from the optimum of the expectation value of Hamiltonian. Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS) algorithm is proposed. First of all, this paper formulates a wireless scheduling problem using maximum weight independent set (MWIS). Then, for the given MWIS, the proposed QAOS designs the Hamiltonian of the problem. After that, the iterative QAOS sequence solves the wireless scheduling problem. This paper verifies the novelty of the proposed QAOS via simulations implemented by Cirq and TensorFlow-Quantum.

中文翻译：

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无线调度的量子逼近

本文提出了一种用于无线调度问题的量子近似优化算法（QAOA）方法。QAOA 是许多应用中很有前途的混合量子经典算法之一，它在 NP 难题中提供了高度准确的优化解决方案。QAOA 将给定的问题映射到希尔伯特空间，然后为给定的目标和约束生成哈密顿量。然后，QAOA 从经典优化方法中找到合适的参数，以优化生成的哈密顿量的期望值。根据这些参数，可以从哈密顿量的期望值的最优值中得到给定问题的最优解。受QAOA的启发，提出了一种调度量子近似优化（QAOS）算法。首先，本文使用最大权重独立集 (MWIS) 来制定无线调度问题。然后，对于给定的 MWIS，建议的 QAOS 设计问题的哈密顿量。之后，迭代QAOS序列解决了无线调度问题。本文通过 Cirq 和 TensorFlow-Quantum 实现的模拟验证了所提出的 QAOS 的新颖性。