Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm
Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm
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DOI：
10.1109/qce52317.2021.00016
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Xinwei Lee;Yoshiyuki Saito;DongSheng Cai;Nobuyoshi Asai
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文献类型：
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作者：
Xinwei Lee;Yoshiyuki Saito;DongSheng Cai;Nobuyoshi Asai
量子近似优化算法（QAOA）在求解近期噪声中间可扩展量子（NISQ）器件的组合优化问题中有着广泛的应用前景。QAOA具有量子-经典混合结构。它的量子部分由一个参数化的交替算子animator组成，它的经典部分包括一个优化算法，该算法优化参数以最大化问题哈密顿量的期望值。该期望值高度依赖于参数，这意味着一组好的参数导致精确的解。然而，在大的电路深度QAOA，它是难以实现全局优化，由于多次出现的局部极小值或极大值。在本文中，我们提出了一种参数固定策略，它提供了高的近似比平均，即使在大的电路深度，通过初始化QAOA从以前的深度获得的最佳参数。我们测试我们的策略上的最大切割问题的某些类别的图，如3-正则图和Erdös-Rényi图。
The quantum approximate optimization algorithm (QAOA) has numerous promising applications in solving the combinatorial optimization problems on near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure. Its quantum part consists of a parameterized alternating operator ansatz, and its classical part comprises an optimization algorithm, which optimizes the parameters to maximize the expectation value of the problem Hamiltonian. This expectation value depends highly on the parameters, this implies that a set of good parameters leads to an accurate solution. However, at large circuit depth of QAOA, it is difficult to achieve global optimization due to the multiple occurrences of local minima or maxima. In this paper, we propose a parameters fixing strategy which gives high approximation ratio on average, even at large circuit depths, by initializing QAOA with the optimal parameters obtained from the previous depths. We test our strategy on the Max-cut problem of certain classes of graphs such as the 3-regular graphs and the Erdös-Rényi graphs.