量子强化算法优化
Optimization by a quantum reinforcement algorithm
摘要：
强化算法通过向系统引入反馈来解决经典优化问题，该反馈缓慢地改变能量景观并将算法收敛到配置空间中的最优解。在这里，我们使用这种策略集中（本地化）优先的波函数的量子粒子，探索配置空间的问题，在一个最佳的配置。我们研究的方法，通过数值求解方程的演变系统，这是类似的非线性薛定谔方程，小问题的大小。特别是，我们观察到，加固增加了量子退火算法中的系统的最小能隙。我们的数值模拟和后者的观察表明，这种量子反馈可能有助于解决计算困难的优化问题的量子增强算法。
英文摘要：
A reinforcement algorithm solves a classical optimization problem by introducing a feedback to the system which slowly changes the energy landscape and converges the algorithm to an optimal solution in the configuration space. Here, we use this strategy to concentrate (localize) preferentially the wave function of a quantum particle, which explores the configuration space of the problem, on an optimal configuration. We examine the method by solving numerically the equations governing the evolution of the system, which are similar to the nonlinear Schrödinger equations, for small problem sizes. In particular, we observe that reinforcement increases the minimal energy gap of the system in a quantum annealing algorithm. Our numerical simulations and the latter observation show that such kind of quantum feedbacks might be helpful in solving a computationally hard optimization problem by a quantum reinforcement algorithm.
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