论文标题
基于蒙特卡洛树搜索的杂种量子电路的混合优化
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
论文作者
论文摘要
在近期和未来容易耐受性的量子设备上，变异量子算法位于模拟的最前沿。虽然大多数变异量子算法仅涉及连续优化变量，但通过添加某些离散优化变量，有时可以通过添加某些离散优化变量来显着增强变异的代表力，这是广义量子近似优化算法（QAOA）的例证。但是，广义QAOA中的混合离散优化问题对优化构成了挑战。我们提出了一种称为MCTS-QAOA的新算法，该算法将蒙特卡洛树搜索方法与改进的自然策略梯度求解器结合在一起，分别优化量子电路中的离散和连续变量。我们发现，MCTS-QAOA具有出色的噪声弹性特性，并且在挑战广义QAOA实例中的先前算法优于先前的算法。
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only continuous optimization variables, the representational power of the variational ansatz can sometimes be significantly enhanced by adding certain discrete optimization variables, as is exemplified by the generalized quantum approximate optimization algorithm (QAOA). However, the hybrid discrete-continuous optimization problem in the generalized QAOA poses a challenge to the optimization. We propose a new algorithm called MCTS-QAOA, which combines a Monte Carlo tree search method with an improved natural policy gradient solver to optimize the discrete and continuous variables in the quantum circuit, respectively. We find that MCTS-QAOA has excellent noise-resilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA.
