The task of finding an element in an unstructured database is known as spatial search and can be expressed as a quantum walk evolution on a graph. In this article, we modify the usual search problem by adding an extra trapping vertex to the graph, which is only connected to the target element. We study the transfer efficiency of the walker to a trapping site, using the search problem as a case study. Thus, our model offers no computational advantage for the search problem, but focuses on information transport in an open environment with a search Hamiltonian. The walker evolution is a mix between classical and quantum walk search dynamics. The balance between unitary and non-unitary dynamics is tuned with a parameter, and we numerically show that depending on the graph topology and the connectivity of the target element, this hybrid approach can outperform a purely classical or quantum evolution for reaching the trapping site. We show that this behavior is only observed in the presence of an extra trapping site, and that depending on the topology and a tunable parameter controlling the strength of the oracle, a hybrid regime composed of 90% coherent dynamics can lead to either the highest or worst transfer efficiency to the trapping site. We also relate the performance of an hybrid regime to the entropy's decay rate. As the introduction of non-unitary operations may be considered as noise, we interpret this phenomena as a noisy-assisted quantum evolution.

翻译：在非结构化数据库中查找元素的任务被称为空间搜索，可以表示为图上的量子行走演化。本文通过在图中添加一个仅与目标元素相连的额外捕获顶点，对常规搜索问题进行改进。我们以搜索问题为案例，研究行走者向捕获位点的传输效率。因此，本模型虽未为搜索问题提供计算优势，但聚焦于具有搜索哈密顿量的开放环境中的信息传输。行走者演化是经典与量子行走搜索动力学的混合体。通过参数调节幺正与非幺正动力学之间的平衡，我们数值证明：根据图拓扑结构和目标元素的连接性，这种混合方法在抵达捕获位点方面可以超越纯经典或量子演化。我们指出该现象仅在额外捕获位点存在时出现，且根据拓扑结构以及控制预言机强度的可调参数，由90%相干动力学组成的混合机制可能导致捕获位点的最高或最低传输效率。我们还将混合机制的性能与熵衰减率相关联。由于非幺正操作的引入可被视为噪声，我们将此现象解释为噪声辅助的量子演化。