论文标题
量子步行在增强的自由能景观中：量子退火
Quantum walk in a reinforced free-energy landscape: Quantum annealing with reinforcement
论文作者
论文摘要
为量子退火算法提供最佳途径是找到良好的计算硬优化问题的良好近似解决方案的关键。强化是可以在退火过程中规避系统的指数能量差距的策略之一。在这里，将时间依赖性的加固项添加到哈密顿量，以使能量较低，使能量降低到不断发展的系统的最可能状态。在这项研究中，我们在配置空间中采用局部熵进行加固，并将算法应用于许多简单而硬的优化问题。增强算法的性能优于量子搜索问题中的标准量子退火算法，在量子搜索问题中，最佳参数的行为取决于解决方案的数量。此外，这些钢筋可以将平均场P-Spin模型（$ P> 2 $）的不连续相变为连续过渡。该算法在二进制感知问题中的性能也优于标准量子退火算法，该算法已经比经典的模拟退火算法更好。
Providing an optimal path to a quantum annealing algorithm is key to finding good approximate solutions to computationally hard optimization problems. Reinforcement is one of the strategies that can be used to circumvent the exponentially small energy gaps of the system in the annealing process. Here a time-dependent reinforcement term is added to the Hamiltonian in order to give lower energies to the most probable states of the evolving system. In this study, we take a local entropy in the configuration space for the reinforcement and apply the algorithm to a number of easy and hard optimization problems. The reinforced algorithm performs better than the standard quantum annealing algorithm in the quantum search problem, where the optimal parameters behave very differently depending on the number of solutions. Moreover, the reinforcements can change the discontinuous phase transitions of the mean-field p-spin model ($p>2$) to a continuous transition. The algorithm's performance in the binary perceptron problem is also superior to that of the standard quantum annealing algorithm, which already works better than a classical simulated annealing algorithm.
